From 2f50dee3c690237b877c75d4716c146a70b15309 Mon Sep 17 00:00:00 2001 From: hanlei <164718149@qq.com> Date: Mon, 29 May 2023 10:15:06 +0800 Subject: [PATCH] PlatEMO4.2 --- .../C-TSEA/CTSEA.m | 55 +++++ .../C-TSEA/CalDistance.m | 31 +++ .../C-TSEA/CalFitness.m | 57 +++++ .../C-TSEA/EnvironmentalSelection1.m | 62 +++++ .../C-TSEA/EnvironmentalSelection2.m | 47 ++++ .../C-TSEA/MatingSelection1.m | 39 ++++ .../C-TSEA/MatingSelection2.m | 18 ++ .../C-TSEA/UpdateArchive.m | 55 +++++ .../C-TSEA/UpdateRefPoint.m | 64 ++++++ .../CCGDE3/CCGDE3.m | 1 + .../CCGDE3/GDE3_EnvironmentalSelection.m | 53 +++-- .../Multi-objective optimization/CMME/CMME.m | 38 +++ .../CMME/CalFitness1.m | 58 +++++ .../CMME/CalFitness2.m | 48 ++++ .../CMME/DensityEstimate.m | 23 ++ .../CMME/EnvironmentalSelection.m | 91 ++++++++ .../CMME/tNDSort.m | 69 ++++++ .../Multi-objective optimization/CMMO/CMMO.m | 67 ++++++ .../CMMO/CalFitness.m | 54 +++++ .../CMMO/CalFitnessDecEpsilon.m | 49 ++++ .../CMMO/EnvironmentalSelection.m | 59 +++++ .../CMMO/EnvironmentalSelectionDec.m | 51 +++++ .../CMOCSO/CMOCSO.m | 90 ++++++++ .../CMOCSO/CalFitness.m | 55 +++++ .../CMOCSO/CompetitiveOperator.m | 47 ++++ .../CMOCSO/CooperativeOperator.m | 58 +++++ .../CMOCSO/UpdateP.m | 44 ++++ .../CMOCSO/UpdateP1.m | 45 ++++ .../CMOCSO/UpdateP2.m | 46 ++++ .../CMOEMT/CMOEMT.m | 216 ++++++++++++++++++ .../CMOEMT/CalFitness.m | 58 +++++ .../CMOEMT/EnvironmentalSelectionT1.m | 48 ++++ .../CMOEMT/EnvironmentalSelectionT2.m | 129 +++++++++++ .../CMOEMT/EnvironmentalSelectionT3.m | 48 ++++ .../CMOQLMT/CMOQLMT.m | 183 +++++++++++++++ .../CMOQLMT/CalFitness.m | 58 +++++ .../CMOQLMT/EnvironmentalSelectionT1.m | 48 ++++ .../CMOQLMT/EnvironmentalSelectionT2.m | 129 +++++++++++ .../CMOQLMT/EnvironmentalSelectionT3.m | 48 ++++ .../CMOQLMT/ICMA_Update.m | 65 ++++++ .../CMOQLMT/Indicator_based_CHT.m | 72 ++++++ .../CMOQLMT/Neighbor_Pairing_Strategy.m | 39 ++++ .../CMOQLMT/Selection_Operator_of_PREA.m | 121 ++++++++++ .../Multi-objective optimization/CSEA/CSEA.m | 29 ++- .../Multi-objective optimization/CSEA/FNN.m | 78 ------- .../CSEA/SurrogateAssistedSelection.m | 11 +- .../DP-PPS/CalFitness.m | 58 +++++ .../DP-PPS/DPPPS.m | 170 ++++++++++++++ .../DP-PPS/EnvironmentalSelection.m | 122 ++++++++++ .../DP-PPS/EnvironmentalSelection_noCon.m | 126 ++++++++++ .../DP-PPS/archive.m | 44 ++++ .../HHC-MMEA/Divide.m | 29 +++ .../HHC-MMEA/DivisionFlag.m | 42 ++++ .../HHC-MMEA/EnvironmentalSelection.m | 38 +++ .../HHC-MMEA/HHCMMEA.m | 186 +++++++++++++++ .../HHC-MMEA/Leader.m | 20 ++ .../HHC-MMEA/Operator1.m | 63 +++++ .../HHC-MMEA/Operator2.m | 74 ++++++ .../HHC-MMEA/SubPopRank.m | 28 +++ .../HHC-MMEA/SubPopSimility.m | 32 +++ .../HHC-MMEA/UpdateGV.m | 30 +++ .../MOEA-D-EGO/EGOSelect.m | 2 +- .../PC-SAEA/CalFitnessPC.m | 75 ++++++ .../PC-SAEA/DataProcess.m | 25 ++ .../PC-SAEA/ESCalFitness.m | 45 ++++ .../PC-SAEA/EnvironmentalSelection.m | 44 ++++ .../PC-SAEA/PCSAEA.m | 60 +++++ .../PC-SAEA/RBFNNPC.m | 84 +++++++ .../PC-SAEA/SurrogateAssistedSelectionPC.m | 61 +++++ .../POCEA/POCEA.m | 2 +- .../Multi-objective optimization/PPS/PPS.m | 2 +- .../SGECF/EnSelection.m | 28 +++ .../SGECF/EnvironmentalSelection.m | 38 +++ .../SGECF/InitRank.m | 60 +++++ .../SGECF/Operator.m | 43 ++++ .../SGECF/OperatorMax.m | 61 +++++ .../SGECF/OperatorMin.m | 61 +++++ .../SGECF/OperatorWin.m | 49 ++++ .../SGECF/SGECF.m | 112 +++++++++ .../SGECF/SPEA2_EnvironmentalSelection.m | 55 +++++ .../TS-NSGA-II/DensityEstimate.m | 23 ++ .../TS-NSGA-II/EnvironmentalSelection.m | 48 ++++ .../TS-NSGA-II/EnvironmentalSelection1.m | 49 ++++ .../TS-NSGA-II/LevelSort.m | 36 +++ .../TS-NSGA-II/SPDSort.m | 65 ++++++ .../TS-NSGA-II/TSNSGAII.m | 44 ++++ .../URCMO/URCMO.m | 2 +- .../Single-objective optimization/GA/GA.m | 2 +- .../Utility functions/OperatorPSO.m | 2 +- PlatEMO/GUI/GUI.m | 4 +- PlatEMO/GUI/module_app.m | 2 +- .../EvoXBench/C10MOP1.m | 2 +- .../EvoXBench/C10MOP2.m | 1 + .../EvoXBench/C10MOP3.m | 1 + .../EvoXBench/C10MOP4.m | 1 + .../EvoXBench/C10MOP5.m | 1 + .../EvoXBench/C10MOP6.m | 1 + .../EvoXBench/C10MOP7.m | 1 + .../EvoXBench/C10MOP8.m | 1 + .../EvoXBench/C10MOP9.m | 1 + .../EvoXBench/IN1KMOP1.m | 1 + .../EvoXBench/IN1KMOP2.m | 1 + .../EvoXBench/IN1KMOP3.m | 1 + .../EvoXBench/IN1KMOP4.m | 1 + .../EvoXBench/IN1KMOP5.m | 1 + .../EvoXBench/IN1KMOP6.m | 1 + .../EvoXBench/IN1KMOP7.m | 1 + .../EvoXBench/IN1KMOP8.m | 1 + .../EvoXBench/IN1KMOP9.m | 1 + .../RWMOPs/RWMOP50.m | 2 +- PlatEMO/manual (Chinese) .pdf | Bin 1499162 -> 1538440 bytes PlatEMO/manual.pdf | Bin 1456424 -> 1501493 bytes 112 files changed, 4998 insertions(+), 123 deletions(-) create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CTSEA.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalDistance.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateArchive.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateRefPoint.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMME/CMME.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMME/DensityEstimate.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMME/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMME/tNDSort.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitnessDecEpsilon.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelectionDec.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CMOCSO.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CompetitiveOperator.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CooperativeOperator.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CMOEMT.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT3.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CMOQLMT.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT3.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/ICMA_Update.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Indicator_based_CHT.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Neighbor_Pairing_Strategy.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Selection_Operator_of_PREA.m delete mode 100644 PlatEMO/Algorithms/Multi-objective optimization/CSEA/FNN.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/CalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/DPPPS.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection_noCon.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/archive.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Divide.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/DivisionFlag.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/HHCMMEA.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Leader.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator2.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/SubPopRank.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/SubPopSimility.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/UpdateGV.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/CalFitnessPC.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/DataProcess.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/ESCalFitness.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/PCSAEA.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/RBFNNPC.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/SurrogateAssistedSelectionPC.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/InitRank.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/Operator.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMax.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMin.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorWin.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/SGECF.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/SGECF/SPEA2_EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/DensityEstimate.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/EnvironmentalSelection.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/EnvironmentalSelection1.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/LevelSort.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/SPDSort.m create mode 100644 PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/TSNSGAII.m diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CTSEA.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CTSEA.m new file mode 100644 index 000000000..b6ee33631 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CTSEA.m @@ -0,0 +1,55 @@ +classdef CTSEA < ALGORITHM +% +% Constrained two-stage evolutionary algorithm + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, H. Zhen, S. Li, L. Wang, and Z. Liao, A simple +% two-stage evolutionary algorithm for constrained multi-objective +% optimization, Knowledge-Based Systems, 2021, 228: 107263. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + %% Generate the sampling points and random population + Population = Problem.Initialization(); + W = UniformPoint(Problem.N,Problem.M); + [ARMOEA_Archive,RefPoint,Range] = UpdateRefPoint(Population.objs,W,[]); + CV = sum(max(0,Population.cons),2); + Archive = Population(CV==0); + stage_conver = 0; + + %% Optimization + while Algorithm.NotTerminated(Population) + if Problem.FE<0.5*Problem.maxFE + % evolve population to PF by ARMOEA + MatingPool = MatingSelection1(Population,RefPoint,Range); + Offspring = OperatorGA(Problem,Population(MatingPool)); + [ARMOEA_Archive,RefPoint,Range] = UpdateRefPoint([ARMOEA_Archive;Offspring.objs],W,Range); + Archive = UpdateArchive(Archive,[Population,Offspring],Problem.N); + [Population,Range] = EnvironmentalSelection1([Population,Offspring],RefPoint,Range,Problem.N); + else + if stage_conver==0 + % exchange archive and population + temp = Population; + Population = Archive; + Archive = temp; + stage_conver = 1; + end + % evolve population to CPF by modified SPEA2 + MatingPool = MatingSelection2(Population,Archive,Problem.N); + Offspring = OperatorGA(Problem,MatingPool); + Population = EnvironmentalSelection2([Population,Offspring],Problem.N); + end + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalDistance.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalDistance.m new file mode 100644 index 000000000..f30caaaca --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalDistance.m @@ -0,0 +1,31 @@ +function Distance = CalDistance(PopObj,RefPoint) +% Calculate the distance between each solution to each adjusted reference point + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + NR = size(RefPoint,1); + PopObj = max(PopObj,1e-6); + RefPoint = max(RefPoint,1e-6); + + %% Adjust the location of each reference point + Cosine = 1 - pdist2(PopObj,RefPoint,'cosine'); + NormR = sqrt(sum(RefPoint.^2,2)); + NormP = sqrt(sum(PopObj.^2,2)); + d1 = repmat(NormP,1,NR).*Cosine; + d2 = repmat(NormP,1,NR).*sqrt(1-Cosine.^2); + [~,nearest] = min(d2,[],1); + RefPoint = RefPoint.*repmat(d1(N.*(0:NR-1)+nearest)'./NormR,1,size(RefPoint,2)); + + %% Calculate the distance between each solution to each point + Distance = pdist2(PopObj,RefPoint); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalFitness.m new file mode 100644 index 000000000..fc3a4e342 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/CalFitness.m @@ -0,0 +1,57 @@ +function Fitness = CalFitness(PopObj,PopCon) +% Calculate the fitness of each solution + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + if nargin == 1 + CV = zeros(N,1); + else + CV = sum(max(0,PopCon),2); + end + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + if CV(i) < CV(j) + Dominate(i,j) = true; + elseif CV(i) > CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection1.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection1.m new file mode 100644 index 000000000..72578466b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection1.m @@ -0,0 +1,62 @@ +function [Population,Range] = EnvironmentalSelection1(Population,RefPoint,Range,N) +% The environmental selection of AR-MOEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Selection among feasible solutions + % Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,N); + Next = FrontNo < MaxFNo; + % Select the solutions in the last front + Last = find(FrontNo==MaxFNo); + Choose = LastSelection(Population(Last).objs,RefPoint,Range,N-sum(Next)); + Next(Last(Choose)) = true; + Population = Population(Next); + % Update the range for normalization + Range(2,:) = max(Population.objs,[],1); + Range(2,Range(2,:)-Range(1,:)<1e-6) = 1; +end + +function Remain = LastSelection(PopObj,RefPoint,Range,K) +% Select part of the solutions in the last front + + N = size(PopObj,1); + NR = size(RefPoint,1); + + %% Calculate the distance between each solution and point + Distance = CalDistance(PopObj-repmat(Range(1,:),N,1),RefPoint); + Convergence = min(Distance,[],2); + + %% Delete the solution which has the smallest metric contribution one by one + [dis,rank] = sort(Distance,1); + Remain = true(1,N); + while sum(Remain) > K + % Calculate the fitness of noncontributing solutions + Noncontributing = Remain; + Noncontributing(rank(1,:)) = false; + METRIC = sum(dis(1,:)) + sum(Convergence(Noncontributing)); + Metric = inf(1,N); + Metric(Noncontributing) = METRIC - Convergence(Noncontributing); + % Calculate the fitness of contributing solutions + for p = find(Remain & ~Noncontributing) + temp = rank(1,:) == p; + noncontributing = false(1,N); + noncontributing(rank(2,temp)) = true; + noncontributing = noncontributing & Noncontributing; + Metric(p) = METRIC - sum(dis(1,temp)) + sum(dis(2,temp)) - sum(Convergence(noncontributing)); + end + % Delete the worst solution and update the variables + [~,del] = min(Metric); + temp = rank ~= del; + dis = reshape(dis(temp),sum(Remain)-1,NR); + rank = reshape(rank(temp),sum(Remain)-1,NR); + Remain(del) = false; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection2.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection2.m new file mode 100644 index 000000000..defcb08fb --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/EnvironmentalSelection2.m @@ -0,0 +1,47 @@ +function [Population,Fitness] = EnvironmentalSelection2(Population,N) +% The environmental selection of SPEA2 + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs,Population.cons); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection1.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection1.m new file mode 100644 index 000000000..22799268a --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection1.m @@ -0,0 +1,39 @@ +function MatingPool = MatingSelection1(Population,RefPoint,Range) +% The mating selection of AR-MOEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Calculate the fitness of each feasible solution based on IGD-NS + % Calculate the distance between each solution and point + N = length(Population); + Distance = CalDistance(Population.objs-repmat(Range(1,:),N,1),RefPoint); + Convergence = min(Distance,[],2); + [dis,rank] = sort(Distance,1); + % Calculate the fitness of noncontributing solutions + Noncontributing = true(1,N); + Noncontributing(rank(1,:)) = false; + METRIC = sum(dis(1,:)) + sum(Convergence(Noncontributing)); + fitness = inf(1,N); + fitness(Noncontributing) = METRIC - Convergence(Noncontributing); + % Calculate the fitness of contributing solutions + for p = find(~Noncontributing) + temp = rank(1,:) == p; + noncontributing = false(1,N); + noncontributing(rank(2,temp)) = true; + noncontributing = noncontributing & Noncontributing; + fitness(p) = METRIC - sum(dis(1,temp)) + sum(dis(2,temp)) - sum(Convergence(noncontributing)); + end + + %% Combine the fitness of feasible solutions with the fitness of infeasible solutions + Fitness = -inf(1,length(Population)); + + %% Binary tournament selection + MatingPool = TournamentSelection(2,length(Population),-Fitness); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection2.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection2.m new file mode 100644 index 000000000..1228e45cd --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/MatingSelection2.m @@ -0,0 +1,18 @@ +function MatingPool = MatingSelection2(Population,Archive,N) +% The mating selection of stage 2 + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + Fitness1 = CalFitness(Population.objs,Population.cons); + MatingPool1 = TournamentSelection(2,N,Fitness1); + MatingPool = Population(MatingPool1); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateArchive.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateArchive.m new file mode 100644 index 000000000..7d52524bd --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateArchive.m @@ -0,0 +1,55 @@ +function UpdatedArchive=UpdateArchive(Archive,Population,N) +% Update Archive + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + CV = sum(max(0,Population.cons),2); + Archive=[Archive,Population(CV==0)]; + if length(Archive)==N + UpdatedArchive=Archive; + elseif length(Archive) N + Del = Truncation(Archive(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Archive for next generation + UpdatedArchive = Archive(Next); + end +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateRefPoint.m b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateRefPoint.m new file mode 100644 index 000000000..708e07401 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/C-TSEA/UpdateRefPoint.m @@ -0,0 +1,64 @@ +function [Archive,RefPoint,Range] = UpdateRefPoint(Archive,W,Range) +% Reference point adaption + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Delete duplicated and dominated solutions + Archive = unique(Archive(NDSort(Archive,1)==1,:),'rows'); + NA = size(Archive,1); + NW = size(W,1); + + %% Update the ideal point + if ~isempty(Range) + Range(1,:) = min([Range(1,:);Archive],[],1); + elseif ~isempty(Archive) + Range = [min(Archive,[],1);max(Archive,[],1)]; + end + + %% Update archive and reference points + if size(Archive,1) <= 1 + RefPoint = W; + else + %% Find contributing solutions and valid weight vectors + tArchive = Archive - repmat(Range(1,:),NA,1); + W = W.*repmat(Range(2,:)-Range(1,:),NW,1); + Distance = CalDistance(tArchive,W); + [~,nearestP] = min(Distance,[],1); + ContributingS = unique(nearestP); + [~,nearestW] = min(Distance,[],2); + ValidW = unique(nearestW(ContributingS)); + + %% Update archive + Choose = ismember(1:NA,ContributingS); + Cosine = 1 - pdist2(tArchive,tArchive,'cosine'); + Cosine(logical(eye(size(Cosine,1)))) = 0; + while sum(Choose) < min(3*NW,size(tArchive,1)) + unSelected = find(~Choose); + [~,x] = min(max(Cosine(~Choose,Choose),[],2)); + Choose(unSelected(x)) = true; + end + Archive = Archive(Choose,:); + tArchive = tArchive(Choose,:); + + %% Update reference points + RefPoint = [W(ValidW,:);tArchive]; + Choose = [true(1,length(ValidW)),false(1,size(tArchive,1))]; + Cosine = 1 - pdist2(RefPoint,RefPoint,'cosine'); + Cosine(logical(eye(size(Cosine,1)))) = 0; + while sum(Choose) < min(NW,size(RefPoint,1)) + Selected = find(~Choose); + [~,x] = min(max(Cosine(~Choose,Choose),[],2)); + Choose(Selected(x)) = true; + end + RefPoint = RefPoint(Choose,:); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/CCGDE3.m b/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/CCGDE3.m index f1230a6b4..94eda32ad 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/CCGDE3.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/CCGDE3.m @@ -65,6 +65,7 @@ function main(Algorithm,Problem) end end end + function Population = GetInd(Problem,subDec1,subDec2,Index,numSPop,j) Dec = zeros(numSPop,Problem.D); if j == 1 diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/GDE3_EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/GDE3_EnvironmentalSelection.m index 15bfbe3e7..31d152159 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/GDE3_EnvironmentalSelection.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/CCGDE3/GDE3_EnvironmentalSelection.m @@ -1,5 +1,5 @@ function Population = GDE3_EnvironmentalSelection(Population,Offspring,N) -% The environmental selection of CCGDE3 +% The environmental selection of GDE3 %------------------------------- Copyright -------------------------------- % Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for @@ -9,32 +9,39 @@ % for evolutionary multi-objective optimization [educational forum], IEEE % Computational Intelligence Magazine, 2017, 12(4): 73-87". %-------------------------------------------------------------------------- - - % Select the offsprings dominating their corresponding parents firstly, - % and then select the offspring population non-dominated with the - % parent population - PopObj = Population.objs; - OffObj = Offspring.objs; + + %% Select by constraint-domination + PopObj = Population.objs; + PopCon = Population.cons; + feasibleP = all(PopCon<=0,2); + OffObj = Offspring.objs; + OffCon = Offspring.cons; + feasibleO = all(OffCon<=0,2); % The offsprings which can replace its parent - updated = all(PopObj>=OffObj,2); + updated = ~feasibleP&feasibleO | ... + ~feasibleP&~feasibleO & all(PopCon>=OffCon,2) | ... + feasibleP&feasibleO & all(PopObj>=OffObj,2); % The offsprings which can add to the population - selected = any(PopObjOffObj,2); + selected = feasibleP&feasibleO & any(PopObjOffObj,2); % Update the population Population(updated) = Offspring(updated); Population = [Population,Offspring(selected)]; - %% Non-dominated sorting - [FrontNo,MaxFNo] = NDSort(Population.objs,N); - Next = FrontNo < MaxFNo; - - %% Calculate the crowding distance of each solution - CrowdDis = CrowdingDistance(Population.objs,FrontNo); - - %% Select the solutions in the last front based on their crowding distances - Last = find(FrontNo==MaxFNo); - [~,Rank] = sort(CrowdDis(Last),'descend'); - Next(Last(Rank(1:N-sum(Next)))) = true; - - %% Population for next generation - Population = Population(Next); + %% Select by non-dominated sorting and crowding distance + PopObj = Population.objs; + PopCon = Population.cons; + feasible = all(PopCon<=0,2); + % Non-dominated sorting based on constraint-domination + FrontNo = inf(1,length(Population)); + [FrontNo(feasible),MaxFNo] = NDSort(PopObj(feasible,:),inf); + FrontNo(~feasible) = NDSort(PopCon(~feasible,:),inf) + MaxFNo; + % Determine the last front + MaxFNo = find(cumsum(hist(FrontNo,1:max(FrontNo)))>=N,1); + lastFront = find(FrontNo==MaxFNo); + % Eliminate solutions in the last front one by one + while length(lastFront) > N - sum(FrontNo +% Constrained many-objective evolutionary algorithm with enhanced mating and environmental selections + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, L. Wang, and L. Gao, A Constrained many-objective +% optimization evolutionary algorithm with enhanced mating and +% environmental selections, IEEE Transactions on Cybernetics, 2022. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + %% Generate Uniform Reference Points + [W,Problem.N] = UniformPoint(Problem.N,Problem.M); + + %% Generate random population + Population = Problem.Initialization(); + + %% Optimization + while Algorithm.NotTerminated(Population) + density = DensityEstimate(Population.objs,W); + Fitness = CalFitness2(Population.objs,Population.cons); + MatingPool = TournamentSelection(2,Problem.N,Fitness,density); + Offspring = OperatorGA(Problem,Population(MatingPool)); + Population = EnvironmentalSelection([Population,Offspring],W,Problem.N); + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness1.m b/PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness1.m new file mode 100644 index 000000000..1718a5131 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness1.m @@ -0,0 +1,58 @@ +function Fitness = CalFitness1(PopObj,PopCon) +% Calculate the fitness of each solution for environmental selection + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + if nargin == 1 + CV = zeros(N,1); + else + CV = sum(max(0,PopCon),2); + end + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + if CV(i) < CV(j) + Dominate(i,j) = true; + elseif CV(i) > CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness2.m b/PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness2.m new file mode 100644 index 000000000..4b891bdcc --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMME/CalFitness2.m @@ -0,0 +1,48 @@ +function Fitness = CalFitness2(PopObj,PopCon) +% Calculate the fitness of each solution for mating selection + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + CV = sum(max(0,PopCon),2); + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate the fitnesses + for i=1:N + if CV(i)==0||R(i)==0 + Fitness(i)=0; + else + Fitness(i)=CalFitness(PopObj(i,:),PopCon(i,:)); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMME/DensityEstimate.m b/PlatEMO/Algorithms/Multi-objective optimization/CMME/DensityEstimate.m new file mode 100644 index 000000000..41c468a54 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMME/DensityEstimate.m @@ -0,0 +1,23 @@ +function density = DensityEstimate(PopObj,W) +% Estimate the density of each individual + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + Zmax=max(PopObj,[],1); + Zmin=min(PopObj,[],1); + SPopObj=(PopObj-repmat(Zmin,size(PopObj,1),1))./(repmat(Zmax,size(PopObj,1),1)-repmat(Zmin,size(PopObj,1),1)); + [~,Region] = max(1-pdist2(SPopObj,W,'cosine'),[],2); + [value,~]=sort(Region,'ascend'); + flag=max(value); + counter=histc(value,1:flag); + density=counter(Region); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMME/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/CMME/EnvironmentalSelection.m new file mode 100644 index 000000000..7384bd1ec --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMME/EnvironmentalSelection.m @@ -0,0 +1,91 @@ +function Population = EnvironmentalSelection(OffSpring,W,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + S = []; % S is the set used for output (replaced by population as output) + Sc = []; % Sc is used to collect feasible solutions + PopObj = OffSpring.objs; + PopCon = OffSpring.cons; + Fitness = CalFitness1(PopObj,PopCon); + Sc=[Sc,OffSpring(Fitness<1)]; + + if length(Sc)==N + Population=Sc; + elseif length(Sc)>N + [FrontNO,MaxNO]=NDSort(Sc.objs,inf); + if MaxNO==1 + %% ¦È-dominated sorting + FrontNO = tNDSort(Sc.objs,W); + MaxNO=max(FrontNO); + for i=1:MaxNO + S=cat(2,S,Sc(FrontNO==i)); + if length(S)>=N + break; + end + end + else + for i=1:MaxNO + S=cat(2,S,Sc(FrontNO==i)); + if length(S)>=N + break; + end + end + end + while length(S)>N + %normalization + Zmax=max(S.objs,[],1); + Zmin=min(S.objs,[],1); + SPopObj=(S.objs-repmat(Zmin,size(S.objs,1),1))./(repmat(Zmax,size(S.objs,1),1)-repmat(Zmin,size(S.objs,1),1)); + + [~,Region] = max(1-pdist2(SPopObj,W,'cosine'),[],2);% associate each solution in S with their corresponding subregion + [value,~]=sort(Region,'ascend'); + flag=max(value); + counter=histc(value,1:flag); % counter denotes the number of indiviudals in each subregion + [~,most_crowded]=max(counter); + S_crowdest=S(Region==most_crowded); % S_crowdest is the set of individuals from the most crowded subregion + dist=pdist2(S_crowdest.objs,S_crowdest.objs); + dist(dist==0)=inf; + [row,~]=find(min(min(dist))==dist); + St=S_crowdest(row); % St is the set of individuals having the smallest distance in S_crowdest + [~,Region_St] = max(1-pdist2(St.objs,W,'cosine'),[],2); + Z = min(St.objs,[],1); + g_tch=max(abs(St.objs-repmat(Z,length(St),1))./W(Region_St,:),[],2); + [~,order]=max(g_tch); + x_wrost=St(order); + S=setdiff(S,x_wrost); + end + Population=S; + elseif length(Sc)=N + last=i; + break; + end + end + F_last=SI(FrontNO==last); % find the individuals in the last front joined into S + delete_n=size(S,2)-N; + CV=sum(max(0,F_last.cons),2); + [~,index]=sort(CV,'descend'); + x_wrost=F_last(index(1:delete_n)); + S=setdiff(S,x_wrost); + Population=S; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMME/tNDSort.m b/PlatEMO/Algorithms/Multi-objective optimization/CMME/tNDSort.m new file mode 100644 index 000000000..0f096eef4 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMME/tNDSort.m @@ -0,0 +1,69 @@ +function tFrontNo = tNDSort(PopObj,W) +% theta-non-dominated sorting + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + NW = size(W,1); + + [z,znad] = deal(min(PopObj),max(PopObj)); + %% Normalization + PopObj = Normalization(PopObj,z,znad); + + %% Calculate the d1 and d2 values for each solution to each weight + normP = sqrt(sum(PopObj.^2,2)); + Cosine = 1 - pdist2(PopObj,W,'cosine'); + d1 = repmat(normP,1,size(W,1)).*Cosine; + d2 = repmat(normP,1,size(W,1)).*sqrt(1-Cosine.^2); + + %% Clustering + [~,class] = min(d2,[],2); + + %% Sort + theta = zeros(1,NW) + 5; + theta(sum(W>1e-4,2)==1) = 1e6; + tFrontNo = zeros(1,N); + for i = 1 : NW + C = find(class==i); + [~,rank] = sort(d1(C,i)+theta(i)*d2(C,i)); + tFrontNo(C(rank)) = 1 : length(C); + end +end + +function [PopObj,z,znad] = Normalization(PopObj,z,znad) +% Normalize the population and update the ideal point and the nadir point + + [N,M] = size(PopObj); + + %% Update the ideal point + z = min(z,min(PopObj,[],1)); + + %% Update the nadir point + % Identify the extreme points + W = zeros(M) + 1e-6; + W(logical(eye(M))) = 1; + ASF = zeros(N,M); + for i = 1 : M + ASF(:,i) = max(abs((PopObj-repmat(z,N,1))./(repmat(znad-z,N,1)))./repmat(W(i,:),N,1),[],2); + end + [~,extreme] = min(ASF,[],1); + % Calculate the intercepts + Hyperplane = (PopObj(extreme,:)-repmat(z,M,1))\ones(M,1); + a = (1./Hyperplane)' + z; + if any(isnan(a)) || any(a<=z) + a = max(PopObj,[],1); + end + znad = a; + + %% Normalize the population + PopObj = (PopObj-repmat(z,N,1))./(repmat(znad-z,N,1)); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m new file mode 100644 index 000000000..0f76953e3 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CMMO.m @@ -0,0 +1,67 @@ +classdef CMMO < ALGORITHM +% +% Coevolutionary multi-modal multi-objective optimization framework + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, L. Wang, and L. Gao, Balancing convergence and +% diversity in objective and decision spaces for multimodal multi-objective +% optimization, IEEE Transactions on Emerging Topics in Computational +% Intelligence, 2023, 7(2): 474-486. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + + %% Generate random population + Population1 = Problem.Initialization(); + Population2 = Problem.Initialization(); + + %% parameters for epsilon and initialization of epsilon + G = ceil(Problem.maxFE/(2 * Problem.N)); + gen = 1; + Tc = 0.8 * G; + tao = 0.1; + epsilon_0 = sum(max(Population2.objs,[],1),2); + threshold = 0.1; + epsilon_k = epsilon_0; + + %% calculate fitness of populations + [Fitness1,D_Dec,D_Pop] = CalFitness(Population1.objs,Population1.decs); + [Fitness2,D] = CalFitnessDecEpsilon(Population2.objs,Population2.decs,epsilon_k); + + %% Optimization + while Algorithm.NotTerminated(Population1) + % The value of e(k) and the search strategy are set. + if gen < 0.2 * G + epsilon_k = 0; + elseif gen < Tc + epsilon_k = (1 - tao) * epsilon_k; + if epsilon_k <= threshold + epsilon_k = (gen/G) * epsilon_0; + end + end + + MatingPool1 = TournamentSelection(2,Problem.N,D_Dec,D_Pop,Fitness1); + MatingPool2 = TournamentSelection(2,Problem.N,D,Fitness2); + Offspring1 = OperatorGAhalf(Problem,Population1(MatingPool1)); + Offspring2 = OperatorGAhalf(Problem,Population2(MatingPool2)); + [Population1,Fitness1,D_Dec,D_Pop] = EnvironmentalSelection([Population1,Offspring1,Offspring2],Problem.N); + [Population2,Fitness2,D] = EnvironmentalSelectionDec([Population2,Offspring1,Offspring2],Problem.N,epsilon_k); + + gen = ceil(Problem.FE/(2 * Problem.N)); + objs = sum(Population2.objs,2); + max_obj = max(objs); + epsilon_0 = min(max_obj,epsilon_0); + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitness.m new file mode 100644 index 000000000..8a9be462a --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitness.m @@ -0,0 +1,54 @@ +function [D_Dec,D_Pop,Fitness] = CalFitness(PopObj,PopDec) +% Calculate the fitness of each solution based on original objective space + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D_Pop = 1./(Distance(:,floor(sqrt(N)))+2); + + Distance = pdist2(PopDec,PopDec); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D_Dec = 1./(Distance(:,floor(sqrt(N)))+2); + + D = D_Pop + D_Dec; + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitnessDecEpsilon.m b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitnessDecEpsilon.m new file mode 100644 index 000000000..d65cff861 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/CalFitnessDecEpsilon.m @@ -0,0 +1,49 @@ +function [Fitness,D] = CalFitnessDecEpsilon(PopObj,PopDec,epsilon) +% Calculate the fitness of each solution based on epsilon objective space + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + k = any(PopObj(j,:)-PopObj(i,:) > epsilon) - any(PopObj(j,:)-PopObj(i,:) < epsilon); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopDec,PopDec); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D_Dec = 1./(Distance(:,floor(sqrt(N)))+2); + + D = D_Dec; + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelection.m new file mode 100644 index 000000000..e559d890e --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelection.m @@ -0,0 +1,59 @@ +function [Population,Fitness,D_Dec,D_Pop] = EnvironmentalSelection(Population,N) +% The environmental selection of SPEA2 based on objective space + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + [D_Dec,D_Pop,Fitness] = CalFitness(Population.objs,Population.decs); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,Population(Next).decs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + D_Dec = D_Dec(Next); + D_Pop = D_Pop(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); + D_Dec = D_Dec(rank); + D_Pop = D_Pop(rank); +end + +function Del = Truncation(PopObj,PopDec,K) +% Select part of the solutions by truncation + + %% Truncation + Distance_Pop = pdist2(PopObj,PopObj); + Distance_Pop(logical(eye(length(Distance_Pop)))) = inf; + + Distance_Dec = pdist2(PopDec,PopDec); + Distance_Dec(logical(eye(length(Distance_Dec)))) = inf; + + D = Distance_Pop + Distance_Dec; + + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(D(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelectionDec.m b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelectionDec.m new file mode 100644 index 000000000..4628638d3 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMMO/EnvironmentalSelectionDec.m @@ -0,0 +1,51 @@ +function [Population,Fitness,D] = EnvironmentalSelectionDec(Population,N,epsilon) +% The environmental selection based on decision space + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + [Fitness,D] = CalFitnessDecEpsilon(Population.objs,Population.decs,epsilon); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).decs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + D = D(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); + D = D(rank); +end + +function Del = Truncation(PopDec,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopDec,PopDec); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopDec,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CMOCSO.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CMOCSO.m new file mode 100644 index 000000000..4a8a3a26b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CMOCSO.m @@ -0,0 +1,90 @@ +classdef CMOCSO < ALGORITHM +% +% Competitive and cooperative swarm optimization constrained multi-objective optimization algorithm + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, D. Li, L. Wang, and L. Gao, A competitive and +% cooperative swarm optimizer for constrained multi-objective optimization +% problems, IEEE Transactions on Evolutionary Computation, 2022. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + %% Generate random population + Population = Problem.Initialization(); + CV = sum(max(0,Population.cons),2); + CVmax = max(CV); + epsilon_0 = CVmax; + epsilon = epsilon_0; + Competitive_Pop = UpdateP1(Population,Problem.N,epsilon); + [Cooperative_Pop,Cooperative_Pop_Fitness] = UpdateP2(Population,Problem.N); + Population = UpdateP(Population,Problem.N); + Tc = 0.9 * ceil(Problem.maxFE/Problem.N); + cp = 2; + alpha = 0.95; + tao = 0.05; + y = 10; + G = Problem.maxFE/Problem.N; + + %% Optimization + while Algorithm.NotTerminated(Population) + gen = ceil(Problem.FE/Problem.N); + CV = sum(max(0,Competitive_Pop.cons),2); + CV_max = max(CV); + CVmax = max([CV_max,CVmax]); + epsilon_0 = CVmax; + rf = sum(CV <= 1e-6) / length(Competitive_Pop); + epsilon = update_epsilon(tao,epsilon,epsilon_0,rf,alpha,gen,Tc,cp); + + Competitive_Pop_Fitness = CalFitness(Competitive_Pop.objs,Competitive_Pop.cons,epsilon); + + if length(Competitive_Pop) >= 2 + Rank = randperm(length(Competitive_Pop),floor(length(Competitive_Pop)/2)*2); + else + Rank = [1,1]; + end + Loser = Rank(1:end/2); + Winner = Rank(end/2+1:end); + Change = Competitive_Pop_Fitness(Loser) <= Competitive_Pop_Fitness(Winner); + Temp = Winner(Change); + Winner(Change) = Loser(Change); + Loser(Change) = Temp; + Offspring1 = CompetitiveOperator(Problem,Competitive_Pop(Loser),Competitive_Pop(Winner),y); + + LearningPool = TournamentSelection(2,Problem.N,Cooperative_Pop_Fitness); + Offspring2 = CooperativeOperator(Problem,Cooperative_Pop(LearningPool)); + + Offspring = [Offspring1,Offspring2]; + + Population = UpdateP([Population,Offspring],Problem.N); + Competitive_Pop = UpdateP1([Competitive_Pop,Offspring],Problem.N,epsilon); + gen = ceil(Problem.FE/Problem.N); + y = (Problem.M)^2*((gen/G)-1)^2+1; + [Cooperative_Pop,Cooperative_Pop_Fitness] = UpdateP2([Offspring,Cooperative_Pop],Problem.N); + end + end + end +end + +function epsilon = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha,gen,Tc,cp) +% Update the value of epsilon + + if gen > Tc + epsilon = 0; + else + if rf < alpha + epsilon = (1 - tao) * epsilon_k; + else + epsilon = epsilon_0 * ((1 - (gen / Tc)) ^ cp); + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CalFitness.m new file mode 100644 index 000000000..1b1574ad8 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CalFitness.m @@ -0,0 +1,55 @@ +function Fitness = CalFitness(PopObj,PopCon,epsilon) +% Calculate the fitness of each solution based on different epsilon + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + CV = sum(max(0,PopCon),2); + CV(CV < epsilon) = 0; + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + if CV(i) < CV(j) + Dominate(i,j) = true; + elseif CV(i) > CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CompetitiveOperator.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CompetitiveOperator.m new file mode 100644 index 000000000..f694b2608 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CompetitiveOperator.m @@ -0,0 +1,47 @@ +function Offspring = CompetitiveOperator(Problem,Loser,Winner,y) +% The competitive swarm optimizer + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Parameter setting + LoserDec = Loser.decs; + WinnerDec = Winner.decs; + [N,D] = size(LoserDec); + LoserVel = Loser.adds(zeros(N,D)); + WinnerVel = Winner.adds(zeros(N,D)); + + %% Competitive swarm optimizer + r1 = repmat(rand(N,1),1,D); + r2 = repmat(rand(N,1),1,D); + OffVel = r1.*LoserVel + r2.*(WinnerDec-LoserDec)*y; + n = randi(2,1,1); + OffDec = LoserDec + OffVel + r1.*(OffVel-LoserVel)*(-1)^n; + + %% Add the winners + OffDec = [OffDec;WinnerDec]; + OffVel = [OffVel;WinnerVel]; + + %% Polynomial mutation + Lower = repmat(Problem.lower,2*N,1); + Upper = repmat(Problem.upper,2*N,1); + disM = 20; + Site = rand(2*N,D) < 1/D; + mu = rand(2*N,D); + temp = Site & mu<=0.5; + OffDec = max(min(OffDec,Upper),Lower); + OffDec(temp) = OffDec(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(OffDec(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5; + OffDec(temp) = OffDec(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-OffDec(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + Offspring = Problem.Evaluation(OffDec,OffVel); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CooperativeOperator.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CooperativeOperator.m new file mode 100644 index 000000000..3aee5b964 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/CooperativeOperator.m @@ -0,0 +1,58 @@ +function Offspring = CooperativeOperator(Problem,Parent,Parameter) +% The cooperative swarm optimizer based on GA formula + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Parameter setting + if nargin > 2 + [proC,disC,proM,disM] = deal(Parameter{:}); + else + [proC,disC,proM,disM] = deal(1,20,1,20); + end + if isa(Parent(1),'SOLUTION') + calObj = true; + Parent = Parent.decs; + else + calObj = false; + end + Parent1 = Parent(1:floor(end/2),:); + Parent2 = Parent(floor(end/2)+1:floor(end/2)*2,:); + [N,D] = size(Parent1); + + %% Genetic operators for real encoding + % Simulated binary crossover + beta = zeros(N,D); + mu = rand(N,D); + beta(mu<=0.5) = (2*mu(mu<=0.5)).^(1/(disC+1)); + beta(mu>0.5) = (2-2*mu(mu>0.5)).^(-1/(disC+1)); + beta = beta.*(-1).^randi([0,1],N,D); + beta(rand(N,D)<0.5) = 1; + beta(repmat(rand(N,1)>proC,1,D)) = 1; + Offspring = [(Parent1+Parent2)/2+beta.*(Parent1-Parent2)/2 + (Parent1+Parent2)/2-beta.*(Parent1-Parent2)/2]; + % Polynomial mutation + Lower = repmat(Problem.lower,2*N,1); + Upper = repmat(Problem.upper,2*N,1); + Site = rand(2*N,D) < proM/D; + mu = rand(2*N,D); + temp = Site & mu<=0.5; + Offspring = min(max(Offspring,Lower),Upper); + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*((2.*mu(temp)+(1-2.*mu(temp)).*... + (1-(Offspring(temp)-Lower(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))-1); + temp = Site & mu>0.5; + Offspring(temp) = Offspring(temp)+(Upper(temp)-Lower(temp)).*(1-(2.*(1-mu(temp))+2.*(mu(temp)-0.5).*... + (1-(Upper(temp)-Offspring(temp))./(Upper(temp)-Lower(temp))).^(disM+1)).^(1/(disM+1))); + + if calObj + Offspring = Problem.Evaluation(Offspring); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP.m new file mode 100644 index 000000000..a844d0958 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP.m @@ -0,0 +1,44 @@ +function Population = UpdateP(Population,N) +% Select feasible and non-dominated solutions + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Select feasible solutions + fIndex = all(Population.cons <= 0,2); + Population = Population(fIndex); + + if isempty(Population) + return + elseif length(Population)>N + Fitness = CalFitness(Population.objs,Population.cons,0); + Next = Fitness < 1; + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + Population = Population(Next); + end +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP1.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP1.m new file mode 100644 index 000000000..b004bd81b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP1.m @@ -0,0 +1,45 @@ +function [Population,Fitness] = UpdateP1(Population,N,epsilon) +% Selecte epsilon feasible solutions + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs,Population.cons,epsilon); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP2.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP2.m new file mode 100644 index 000000000..8a585d668 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOCSO/UpdateP2.m @@ -0,0 +1,46 @@ +function [Population,Fitness] = UpdateP2(Population,N) +% Select non-dominated solutions + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs,Population.cons,inf); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CMOEMT.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CMOEMT.m new file mode 100644 index 000000000..aa6f1a97e --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CMOEMT.m @@ -0,0 +1,216 @@ +classdef CMOEMT < ALGORITHM +% +% Constrained multi-objective optimization based on evolutionary multitasking optimization +% delta --- 0.9 --- The probability of choosing parents locally +% nr --- 2 --- Maximum number of solutions replaced by each offspring + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, L. Wang, and L. Gao, Constrained multi-objective +% optimization via multitasking and knowledge transfer, IEEE Transactions +% on Evolutionary Computation, 2022. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + + %% Parameter setting + [delta,nr] = Algorithm.ParameterSet(0.9,2); + + %% Generate the weight vectors + [W,Problem.N] = UniformPoint(Problem.N,Problem.M); + T = ceil(Problem.N/10); + + %% Detect the neighbours of each solution + B = pdist2(W,W); + [~,B] = sort(B,2); + B = B(:,1:T); + + %% Generate random population + Population{1} = Problem.Initialization(); + Population{2} = Problem.Initialization(); + Z = min(Population{2}.objs,[],1); + Population{3} = Problem.Initialization(); + LengthO{1} = Problem.N/2; LengthO{2} = Problem.N/2; LengthO{3} = Problem.N/2; + Fitness{1} = CalFitness(Population{1}.objs,Population{1}.cons); + Fitness{3} = CalFitness(Population{3}.objs); + + %% Evaluate the Population + Tc = 0.9 * ceil(Problem.maxFE/Problem.N); + last_gen = 20; + change_threshold = 1e-1; + search_stage = 1; % 1 for push stage,otherwise,it is in pull stage. + max_change = 1; + epsilon_k = 0; + epsilon_0 = 0; + cp = 2; + alpha1 = 0.95; + tao = 0.05; + ideal_points = zeros(ceil(Problem.maxFE/Problem.N),Problem.M); + nadir_points = zeros(ceil(Problem.maxFE/Problem.N),Problem.M); + + %% Optimization + while Algorithm.NotTerminated(Population{1}) + gen = ceil(Problem.FE/(2*Problem.N)); + pop_cons = Population{2}.cons; + cv = overall_cv(pop_cons); + population = [Population{2}.decs,Population{2}.objs,cv]; + rf = sum(cv <= 1e-6) / Problem.N; + ideal_points(gen,:) = Z; + nadir_points(gen,:) = max(population(:,Problem.D + 1 : Problem.D + Problem.M),[],1); + + % The maximumrate of change of ideal and nadir points rk is calculated. + if gen >= last_gen + max_change = calc_maxchange(ideal_points,nadir_points,gen,last_gen); + end + + % The value of e(k) and the search strategy are set. + if gen < Tc + if max_change <= change_threshold && search_stage == 1 + search_stage = -1; + epsilon_0 = max(population(:,end),[],1); + epsilon_k = epsilon_0; + end + if search_stage == -1 + epsilon_k = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha1,gen,Tc,cp); + end + else + epsilon_k = 0; + end + + if Problem.FE < Problem.maxFE/2 + %non transfer + + % Offspring Reproduction + MatingPool{1} = TournamentSelection(2,2*LengthO{1},Fitness{1}); + Offspring{1} = OperatorGAhalf(Problem,Population{1}(MatingPool{1})); + Offspring{2} = []; + + for subgeneration = 1 : 5 + Bounday = find(sum(W<1e-3,2)==Problem.M-1)'; + Bounday = [Bounday,floor(length(W)/2)]; + I = [Bounday,randi(length(W),1,floor(Problem.N/5)-length(Bounday))]; + for j = 1 : length(I) + i = I(j); + if rand < delta + P = B(i,randperm(size(B,2))); + else + P = randperm(Problem.N); + end + + % Generate an offspring + offspring = OperatorDE(Problem,Population{2}(i),Population{2}(P(1)),Population{2}(P(2))); + Offspring{2} = [Offspring{2},offspring]; + + % Update the ideal point + Z = min(Z,offspring.obj); + + % TCH approach + g_old = max(abs(Population{2}(P).objs-repmat(Z,length(P),1)).*W(P,:),[],2); + g_new = max(repmat(abs(offspring.obj-Z),length(P),1).*W(P,:),[],2); + cv_old = overall_cv(Population{2}(P).cons); + cv_new = overall_cv(offspring.con) * ones(length(P),1); + + if search_stage == 1 % Push Stage + Population{2}(P(find(g_old>=g_new,nr))) = offspring; + else % Pull Stage && An improved epsilon constraint-handling is employed to deal with constraints + Population{2}(P(find(((g_old >= g_new) & (((cv_old <= epsilon_k) & (cv_new <= epsilon_k)) | (cv_old == cv_new)) | (cv_new < cv_old) ), nr))) = offspring; + end + + end + end + + MatingPool{3} = TournamentSelection(2,2*LengthO{3},Fitness{3}); + Offspring{3} = OperatorGAhalf(Problem,Population{3}(MatingPool{3})); + + % Environmental Selection + [Population{1},Fitness{1},~] = EnvironmentalSelectionT1([Population{1},Offspring{1:3}],Problem.N); + [Population{3},Fitness{3},~] = EnvironmentalSelectionT3([Population{3},Offspring{1:3}],Problem.N); + + else + %transfer + % Offspring Reproduction + MatingPool{1} = TournamentSelection(2,2*LengthO{1},Fitness{1}); + Offspring{1} = OperatorGAhalf(Problem,Population{1}(MatingPool{1})); + Offspring{2} = []; + + for subgeneration = 1 : 5 + Bounday = find(sum(W<1e-3,2)==Problem.M-1)'; + Bounday = [Bounday,floor(length(W)/2)]; + I = [Bounday,randi(length(W),1,floor(Problem.N/5)-length(Bounday))]; + for j = 1 : length(I) + i = I(j); + + % for i = 1 : Problem.N + % Choose the parents + if rand < delta + P = B(i,randperm(size(B,2))); + else + P = randperm(Problem.N); + end + + % Generate an offspring + offspring = OperatorDE(Problem,Population{2}(i),Population{2}(P(1)),Population{2}(P(2))); + Offspring{2} = [Offspring{2},offspring]; + + % Update the ideal point + Z = min(Z,offspring.obj); + + % TCH approach + g_old = max(abs(Population{2}(P).objs-repmat(Z,length(P),1)).*W(P,:),[],2); + g_new = max(repmat(abs(offspring.obj-Z),length(P),1).*W(P,:),[],2); + cv_old = overall_cv(Population{2}(P).cons); + cv_new = overall_cv(offspring.con) * ones(length(P),1); + + if search_stage == 1 % Push Stage + Population{2}(P(find(g_old>=g_new,nr))) = offspring; + else % Pull Stage && An improved epsilon constraint-handling is employed to deal with constraints + Population{2}(P(find(((g_old >= g_new) & (((cv_old <= epsilon_k) & (cv_new <= epsilon_k)) | (cv_old == cv_new)) | (cv_new < cv_old) ), nr))) = offspring; + end + + end + end + + MatingPool{3} = TournamentSelection(2,2*LengthO{3},Fitness{3}); + Offspring{3} = OperatorGAhalf(Problem,Population{3}(MatingPool{3})); + + % Environmental Selection + [Population{1},Fitness{1},~] = EnvironmentalSelectionT1([Population{1:3},Offspring{1:3}],Problem.N); + [Population{3},Fitness{3},~] = EnvironmentalSelectionT3([Population{1:3},Offspring{1:3}],Problem.N); + end + end + end + end +end + +function result = overall_cv(cv) +% The Overall Constraint Violation + + cv(cv <= 0) = 0;cv = abs(cv); + result = sum(cv,2); +end + +function max_change = calc_maxchange(ideal_points,nadir_points,gen,last_gen) +% Calculate the Maximum Rate of Change + + delta_value = 1e-6 * ones(1,size(ideal_points,2)); + rz = abs((ideal_points(gen,:) - ideal_points(gen - last_gen + 1,:)) ./ max(ideal_points(gen - last_gen + 1,:),delta_value)); + nrz = abs((nadir_points(gen,:) - nadir_points(gen - last_gen + 1,:)) ./ max(nadir_points(gen - last_gen + 1,:),delta_value)); + max_change = max([rz, nrz]); +end + +function result = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha,gen,Tc,cp) + if rf < alpha + result = (1 - tao) * epsilon_k; + else + result = epsilon_0 * ((1 - (gen / Tc)) ^ cp); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CalFitness.m new file mode 100644 index 000000000..b0bd85db3 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/CalFitness.m @@ -0,0 +1,58 @@ +function Fitness = CalFitness(PopObj,PopCon) +% Calculate the fitness of each solution + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + if nargin == 1 + CV = zeros(N,1); + else + CV = sum(max(0,PopCon),2); + end + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + if CV(i) < CV(j) + Dominate(i,j) = true; + elseif CV(i) > CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT1.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT1.m new file mode 100644 index 000000000..8a0b0d7eb --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT1.m @@ -0,0 +1,48 @@ +function [Population,Fitness,Next] = EnvironmentalSelectionT1(Population,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs,Population.cons); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT2.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT2.m new file mode 100644 index 000000000..8a1224780 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT2.m @@ -0,0 +1,129 @@ +function [Population,Fitness,Next] = EnvironmentalSelectionT2(Population,N,alpha,gamma,para) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Mengjun Ming + + %% Parameter + popSize = length(Population); + NumSeq = (1:popSize)'; + RankConvg = zeros(popSize,1); + RankDivs = zeros(popSize,1); + + %% Modify the infeasible solutions + PopObj = Population.objs; + PopCon = Population.cons; + z = min(PopObj,[],1); + n = max(PopObj,[],1); + + Infeasible_all = any(PopCon>0,2); + phi_max = max(sum(max(0,PopCon(Infeasible_all,:)),2)); + + M = length(z); + [W,~] = UniformPoint(N,M); + [~,Region] = min(pdist2(PopObj-z,W,'cosine'),[],2); + PopObj_2 = PopObj; + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + Cons_temp = PopCon(index,:); + Infeasible = any(Cons_temp>0,2); + if (sum(Infeasible)~=0) + F_max = max(Objs_temp,[],1); + PopObj_2(index(Infeasible),:) = Objs_temp(Infeasible,:)+(sum(max(0,Cons_temp(Infeasible,:)),2)/phi_max).^(exp(para)/max(gamma,0.000001)).*(F_max-Objs_temp(Infeasible,:)); + end + end + end + + %% Non-dominated sorting + Dominate = false(popSize); + for i = 1 : popSize-1 + for j = i+1 : popSize + k = any(PopObj_2(i,:)PopObj_2(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,popSize); + for i = 1 : popSize + R(i) = sum(S(Dominate(:,i))); + end + FrontNo = R + 1; + + %% Calculate the crowding distance of each solution + PopObj = Population.objs; + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + CrowdDis = Distance(:,floor(sqrt(popSize))); + + %% Add a middle column + MiddleLevel = zeros(popSize,1); + + %% Environmental selection + Next1 = FrontNo == 1; + if sum(Next1) <= N + [~,indx_Convg] = sortrows([FrontNo',-CrowdDis]); + elseif sum(Next1) > N + Del = Truncation(Population(Next1).objs,sum(Next1)-N); + Temp = find(Next1); + Next1(Temp(Del)) = false; + MiddleLevel(Temp(Del)) = 1; + [~,indx_Convg] = sortrows([FrontNo',MiddleLevel,-CrowdDis]); + end + RankConvg(indx_Convg) = NumSeq; + + %% Environmental selection -- diversity + FrontNo_D = ones(popSize,1); + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + g_temp = sum((Objs_temp-z).*W(i,:),2); + [~,index_FrontNo_D] = sort(g_temp); + FrontNo_D(index(index_FrontNo_D)) = (1:length(g_temp))'; + end + end + [~,indx_divs] = sortrows([FrontNo_D,-CrowdDis]); + RankDivs(indx_divs) = NumSeq; + + %% Population for next generation + RankSolution = alpha*RankConvg+(1-alpha)*RankDivs; + [~,Rank] = sort(RankSolution); + Next = zeros(1,length(Population)); + Population = Population(Rank(1:N)); + Fitness = RankSolution(Rank(1:N)); + Choosed = Rank(1:N); + Next(Choosed) = 1; +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT3.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT3.m new file mode 100644 index 000000000..54cd161c8 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOEMT/EnvironmentalSelectionT3.m @@ -0,0 +1,48 @@ +function [Population,Fitness,Next] = EnvironmentalSelectionT3(Population,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CMOQLMT.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CMOQLMT.m new file mode 100644 index 000000000..f36ab95ae --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CMOQLMT.m @@ -0,0 +1,183 @@ +classdef CMOQLMT < ALGORITHM +% +% Constrained multi-objective optimization based on Q-learning and multitasking +% delta --- 0.9 --- The probability of choosing parents locally +% nr --- 2 --- Maximum number of solutions replaced by each offspring + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, and L. Gao, Adaptive auxiliary task selection for +% multitasking-assisted constrained multi-objective optimization [feature], +% IEEE Computational Intelligence Magazine, 2023, 18(2): 18-30. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + %% Generate random population + Population{1} = Problem.Initialization(); + Population{2} = Problem.Initialization(); + Population{3} = Problem.Initialization(); + alpha1 = 2./(1+exp(1).^(-Problem.FE*10/Problem.maxFE))-1; + para = ceil(Problem.maxFE/Problem.N)/2 - ceil(Problem.FE/Problem.N); + Population{4} = Problem.Initialization(); + Zmin = min(Population{4}.objs,[],1); + Fmin = min(Population{4}(all(Population{4}.cons<=0,2)).objs,[],1); + Fitness{1} = CalFitness(Population{1}.objs,Population{1}.cons); + Fitness{3} = CalFitness(Population{3}.objs); + LengthO{1} = Problem.N/2; LengthO{2} = Problem.N/2; LengthO{3} = Problem.N/2; LengthO{4} = Problem.N/2; + W = UniformPoint(Problem.N,Problem.M); + Ra = 1; + + %% For QL + num_task = 3; + Q_Table = zeros(num_task,num_task); + learning = 0.5; + gama_ql = 0.9; + alpha_ql = 0.8; + greedy_ql = 0.9; + current_state = randi(num_task); + + %% Optimization + while Algorithm.NotTerminated(Population{1}) + if Problem.FE < learning * Problem.maxFE + % choose an action by greedy strategy based on Q table + if rand > greedy_ql || ( Q_Table(current_state,1) == Q_Table(current_state,2) && Q_Table(current_state,1) == Q_Table(current_state,3) ) + action = randi(num_task); + else + [~,action] = max(Q_Table(current_state,:)); + end + + % Re-rank + Population{1} = Population{1}(randperm(Problem.N)); + Population{2} = Population{2}(randperm(Problem.N)); + Lia = ismember(Population{2}.objs,Population{1}.objs, 'rows'); + gamma = 1-sum(Lia)/Problem.N; + [Population{2},Fitness{2},~] = EnvironmentalSelectionT2(Population{2},Problem.N,alpha1,gamma,para); + + % Offspring Reproduction + MatingPool{1} = TournamentSelection(2,2*LengthO{1},Fitness{1}); + Offspring{1} = OperatorGAhalf(Problem,Population{1}(MatingPool{1})); + MatingPool{2} = TournamentSelection(2,2*LengthO{2},Fitness{2}); + Offspring{2} = OperatorGAhalf(Problem,Population{2}(MatingPool{2})); + MatingPool{3} = TournamentSelection(2,2*LengthO{3},Fitness{3}); + Offspring{3} = OperatorGAhalf(Problem,Population{3}(MatingPool{3})); + Nt = floor(Ra*Problem.N); + if length(Population{1}) > Problem.N-Nt + MatingPool{4} = [Population{4}(randsample(Problem.N,Nt)),Population{1}(randsample(Problem.N,Problem.N-Nt))]; + else + MatingPool{4} = Population{4}(randsample(Problem.N,Problem.N)); + end + [Mate1,Mate2,Mate3] = Neighbor_Pairing_Strategy(MatingPool{4},Zmin); + if rand > 0.5 + Offspring{4} = OperatorDE(Problem,Mate1,Mate2,Mate3); + else + Offspring{4} = OperatorDE(Problem,Mate1,Mate2,Mate3,{0.5,0.5,0.5,0.75}); + end + + % determine the transfer rate to update Q table + [~,~,Next1] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{action+1},Offspring{action+1}],Problem.N); + succ_rate = (sum(Next1(length(Population{1})+length(Offspring{1})+1:end))) / (length(Population{action+1})+length(Offspring{action+1})); + Q_Table(current_state,action) = Q_Table(current_state,action) + alpha_ql * (succ_rate + gama_ql * (max(Q_Table(action,:))) - Q_Table(current_state,action)); + + current_state = action; + + % Environmental Selection + alpha1 = 2./(1+exp(1).^(-Problem.FE*10/Problem.maxFE)) - 1; + para = ceil(Problem.maxFE/Problem.N)/2 - ceil(Problem.FE/Problem.N); + Fmin = min([Fmin;Offspring{4}(all(Offspring{4}.cons<=0,2)).objs],[],1); + Zmin = min([Zmin;Offspring{4}.objs],[],1); + [Population{1},Fitness{1},~] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{current_state+1},Offspring{current_state+1}],Problem.N); + [Population{2},~,~] = EnvironmentalSelectionT2([Population{2},Offspring{2}],Problem.N,alpha1,gamma,para); + [Population{3},Fitness{3},~] = EnvironmentalSelectionT3([Population{3},Offspring{3}],Problem.N); + [Population{4},~] = ICMA_Update([Population{4},Offspring{4}],Problem.N,W,Zmin,Fmin); + + else + % choose an action by greedy strategy based on Q table + if rand > greedy_ql || ( Q_Table(current_state,1) == Q_Table(current_state,2) && Q_Table(current_state,1) == Q_Table(current_state,3) ) + action = randi(num_task); + else + [~,action] = max(Q_Table(current_state,:)); + end + + % Offspring Reproduction + MatingPool{1} = TournamentSelection(2,2*LengthO{1},Fitness{1}); + Offspring{1} = OperatorGAhalf(Problem,Population{1}(MatingPool{1})); + + if action == 1 + % Re-rank + Population{1} = Population{1}(randperm(Problem.N)); + Population{2} = Population{2}(randperm(Problem.N)); + Lia = ismember(Population{2}.objs,Population{1}.objs, 'rows'); + gamma = 1-sum(Lia)/Problem.N; + [Population{2},Fitness{2},~] = EnvironmentalSelectionT2(Population{2},Problem.N,alpha1,gamma,para); + MatingPool{2} = TournamentSelection(2,2*LengthO{2},Fitness{2}); + Offspring{2} = OperatorGAhalf(Problem,Population{2}(MatingPool{2})); + + % determine the transfer rate to update Q table + [~,~,Next1] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{action+1},Offspring{action+1}],Problem.N); + succ_rate = (sum(Next1(length(Population{1})+length(Offspring{1})+1:end))) / (length(Population{action+1})+length(Offspring{action+1})); + Q_Table(current_state,action) = Q_Table(current_state,action) + alpha_ql * (succ_rate + gama_ql * (max(Q_Table(action,:))) - Q_Table(current_state,action)); + + current_state = action; + + % Environmental Selection + alpha1 = 2./(1+exp(1).^(-Problem.FE*10/Problem.maxFE)) - 1; + para = ceil(Problem.maxFE/Problem.N)/2 - ceil(Problem.FE/Problem.N); + [Population{1},Fitness{1},~] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{current_state+1},Offspring{current_state+1}],Problem.N); + [Population{2},~,~] = EnvironmentalSelectionT2([Population{2},Offspring{2}],Problem.N,alpha1,gamma,para); + elseif action == 2 + MatingPool{3} = TournamentSelection(2,2*LengthO{3},Fitness{3}); + Offspring{3} = OperatorGAhalf(Problem,Population{3}(MatingPool{3})); + + % determine the transfer rate to update Q table + [~,~,Next1] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{action+1},Offspring{action+1}],Problem.N); + succ_rate = (sum(Next1(length(Population{1})+length(Offspring{1})+1:end))) / (length(Population{action+1})+length(Offspring{action+1})); + Q_Table(current_state,action) = Q_Table(current_state,action) + alpha_ql * (succ_rate + gama_ql * (max(Q_Table(action,:))) - Q_Table(current_state,action)); + + current_state = action; + + % Environmental Selection + [Population{1},Fitness{1},~] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{current_state+1},Offspring{current_state+1}],Problem.N); + [Population{3},Fitness{3},~] = EnvironmentalSelectionT3([Population{3},Offspring{3}],Problem.N); + else + Nt = floor(Ra*Problem.N); + if length(Population{1}) > Problem.N-Nt + MatingPool{4} = [Population{4}(randsample(Problem.N,Nt)),Population{1}(randsample(Problem.N,Problem.N-Nt))]; + else + MatingPool{4} = Population{4}(randsample(Problem.N,Problem.N)); + end + + [Mate1,Mate2,Mate3] = Neighbor_Pairing_Strategy(MatingPool{4},Zmin); + if rand > 0.5 + Offspring{4} = OperatorDE(Problem,Mate1,Mate2,Mate3); + else + Offspring{4} = OperatorDE(Problem,Mate1,Mate2,Mate3,{0.5,0.5,0.5,0.75}); + end + + % determine the transfer rate to update Q table + [~,~,Next1] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{action+1},Offspring{action+1}],Problem.N); + succ_rate = (sum(Next1(length(Population{1})+length(Offspring{1})+1:end))) / (length(Population{action+1})+length(Offspring{action+1})); + Q_Table(current_state,action) = Q_Table(current_state,action) + alpha_ql * (succ_rate + gama_ql * (max(Q_Table(action,:))) - Q_Table(current_state,action)); + + current_state = action; + + % Environmental Selection + Fmin = min([Fmin;Offspring{4}(all(Offspring{4}.cons<=0,2)).objs],[],1); + Zmin = min([Zmin;Offspring{4}.objs],[],1); + [Population{1},Fitness{1},~] = EnvironmentalSelectionT1([Population{1},Offspring{1},Population{current_state+1},Offspring{current_state+1}],Problem.N); + [Population{4},~] = ICMA_Update([Population{4},Offspring{4}],Problem.N,W,Zmin,Fmin); + end + end + Ra = 1 - Problem.FE/Problem.maxFE; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CalFitness.m new file mode 100644 index 000000000..b0bd85db3 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/CalFitness.m @@ -0,0 +1,58 @@ +function Fitness = CalFitness(PopObj,PopCon) +% Calculate the fitness of each solution + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + N = size(PopObj,1); + if nargin == 1 + CV = zeros(N,1); + else + CV = sum(max(0,PopCon),2); + end + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + if CV(i) < CV(j) + Dominate(i,j) = true; + elseif CV(i) > CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT1.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT1.m new file mode 100644 index 000000000..8a0b0d7eb --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT1.m @@ -0,0 +1,48 @@ +function [Population,Fitness,Next] = EnvironmentalSelectionT1(Population,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs,Population.cons); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT2.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT2.m new file mode 100644 index 000000000..8a1224780 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT2.m @@ -0,0 +1,129 @@ +function [Population,Fitness,Next] = EnvironmentalSelectionT2(Population,N,alpha,gamma,para) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Mengjun Ming + + %% Parameter + popSize = length(Population); + NumSeq = (1:popSize)'; + RankConvg = zeros(popSize,1); + RankDivs = zeros(popSize,1); + + %% Modify the infeasible solutions + PopObj = Population.objs; + PopCon = Population.cons; + z = min(PopObj,[],1); + n = max(PopObj,[],1); + + Infeasible_all = any(PopCon>0,2); + phi_max = max(sum(max(0,PopCon(Infeasible_all,:)),2)); + + M = length(z); + [W,~] = UniformPoint(N,M); + [~,Region] = min(pdist2(PopObj-z,W,'cosine'),[],2); + PopObj_2 = PopObj; + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + Cons_temp = PopCon(index,:); + Infeasible = any(Cons_temp>0,2); + if (sum(Infeasible)~=0) + F_max = max(Objs_temp,[],1); + PopObj_2(index(Infeasible),:) = Objs_temp(Infeasible,:)+(sum(max(0,Cons_temp(Infeasible,:)),2)/phi_max).^(exp(para)/max(gamma,0.000001)).*(F_max-Objs_temp(Infeasible,:)); + end + end + end + + %% Non-dominated sorting + Dominate = false(popSize); + for i = 1 : popSize-1 + for j = i+1 : popSize + k = any(PopObj_2(i,:)PopObj_2(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,popSize); + for i = 1 : popSize + R(i) = sum(S(Dominate(:,i))); + end + FrontNo = R + 1; + + %% Calculate the crowding distance of each solution + PopObj = Population.objs; + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + CrowdDis = Distance(:,floor(sqrt(popSize))); + + %% Add a middle column + MiddleLevel = zeros(popSize,1); + + %% Environmental selection + Next1 = FrontNo == 1; + if sum(Next1) <= N + [~,indx_Convg] = sortrows([FrontNo',-CrowdDis]); + elseif sum(Next1) > N + Del = Truncation(Population(Next1).objs,sum(Next1)-N); + Temp = find(Next1); + Next1(Temp(Del)) = false; + MiddleLevel(Temp(Del)) = 1; + [~,indx_Convg] = sortrows([FrontNo',MiddleLevel,-CrowdDis]); + end + RankConvg(indx_Convg) = NumSeq; + + %% Environmental selection -- diversity + FrontNo_D = ones(popSize,1); + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + g_temp = sum((Objs_temp-z).*W(i,:),2); + [~,index_FrontNo_D] = sort(g_temp); + FrontNo_D(index(index_FrontNo_D)) = (1:length(g_temp))'; + end + end + [~,indx_divs] = sortrows([FrontNo_D,-CrowdDis]); + RankDivs(indx_divs) = NumSeq; + + %% Population for next generation + RankSolution = alpha*RankConvg+(1-alpha)*RankDivs; + [~,Rank] = sort(RankSolution); + Next = zeros(1,length(Population)); + Population = Population(Rank(1:N)); + Fitness = RankSolution(Rank(1:N)); + Choosed = Rank(1:N); + Next(Choosed) = 1; +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT3.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT3.m new file mode 100644 index 000000000..54cd161c8 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/EnvironmentalSelectionT3.m @@ -0,0 +1,48 @@ +function [Population,Fitness,Next] = EnvironmentalSelectionT3(Population,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Calculate the fitness of each solution + Fitness = CalFitness(Population.objs); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); + % Sort the population + [Fitness,rank] = sort(Fitness); + Population = Population(rank); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/ICMA_Update.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/ICMA_Update.m new file mode 100644 index 000000000..42e6cf4b2 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/ICMA_Update.m @@ -0,0 +1,65 @@ +function [Population,Archive] = ICMA_Update(MaxPop,N,W,Zmin,Fmin) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Jiawei Yuan + + PopObj = MaxPop.objs; + [Num,M] = size(PopObj); + Cons = max(0,MaxPop.cons); + NormCons = Cons./repmat(max(1,max(Cons,[],1)),Num,1); + + CV = sum(NormCons,2); + + % shift the objective space to R+ + PopObj = PopObj - repmat(Zmin,Num,1) + 1e-6; + + % calculate the indicator matrix + IMatrix = ones(Num,Num); + for i = 1:1:Num + Ci = CV(i); + if Ci == 0 %%%%% Xi is feasible + Fi = PopObj(i,:); + Ir = log(repmat(Fi,Num,1)./PopObj); + MaxIr = max(Ir,[],2); + MinIr = min(Ir,[],2); + CVA = MaxIr; + DomInds = find(MaxIr<=0); + CVA(DomInds) = MinIr(DomInds); + IndicatorV = CVA; + else %%%%% Xi is an infeasible solution + IC = repmat(Ci+1e-6,Num,1)./(CV+1e-6); + Fi = PopObj(i,:); + MaxF = max(repmat(Fi,Num,1),PopObj); + MinF = min(repmat(Fi,Num,1),PopObj); + CVF = max(MaxF./MinF,[],2); + IndicatorV = log(max([CVF,IC],[],2)); + end + IMatrix(:,i) = IndicatorV; + IMatrix(i,i) = Inf; + end + + FeasibleInd = find(CV==0); + Len_F = length(FeasibleInd); + + if Len_F<=N + [~,CV_SortInd] = sort(CV); + Archive = MaxPop(CV_SortInd(1:N)); + else + FPopObj = PopObj(FeasibleInd,:) + repmat(Zmin,Len_F,1) - repmat(Fmin,Len_F,1); + SelInd = Selection_Operator_of_PREA(FPopObj,IMatrix(FeasibleInd,FeasibleInd),N); + Archive = MaxPop(FeasibleInd(SelInd)); + end + + %%%%%%%%%%%%% using indicator-based CHT to update the population + SelInd = Indicator_based_CHT(PopObj,IMatrix,W,N); + Population = MaxPop(SelInd); + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Indicator_based_CHT.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Indicator_based_CHT.m new file mode 100644 index 000000000..57899dd20 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Indicator_based_CHT.m @@ -0,0 +1,72 @@ +function SelInd = Indicator_based_CHT(PopObj,IMatrix,W,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Jiawei Yuan + + % calculate the size of individuals in the promising areas + IrFitness = min(IMatrix,[],2); + Level1Index = find(IrFitness>=0); + Len_Level1 = length(Level1Index); + + if Len_Level1<=N + [~,SortIndex] = sort(-IrFitness); + SelInd = SortIndex(1:N); + else + % only focus on the solutions in the promising areas + SelInd = Level1Index; + PopObj = PopObj(Level1Index,:); + IMatrix = IMatrix(Level1Index,Level1Index); + + [Num,M] = size(PopObj); + NormW = W./repmat(sqrt(sum(W.^2,2)),1,M); + NormPopObj = PopObj./repmat(sqrt(sum(PopObj.^2,2)),1,M); + [~,ZoneIndex] = max(NormPopObj * NormW',[],2); + Num_W = size(W,1); + ZoneDensity = zeros(1,Num_W); + zone.index = []; + Zone = repmat(zone,1,Num_W); + for j = 1:Num + Zj = ZoneIndex(j); + Zone(Zj).index = [Zone(Zj).index,j]; + ZoneDensity(Zj) = ZoneDensity(Zj) + 1; + end + + [NDensity,SortIndex] = sort(-ZoneDensity); + Density = abs(NDensity); + [Values,Neightboor] = min(IMatrix,[],2); + + DelNum = Num - N; + Have_Delect = zeros(1,DelNum); + + for i = 1:DelNum + [MDen,MDInd] = max(Density); + CandidateIndex = Zone(SortIndex(MDInd)).index; + + [~,NowDel_Ind] = min(Values(CandidateIndex)); + Del_Ind = CandidateIndex(NowDel_Ind); + CandidateIndex(NowDel_Ind) = []; + Have_Delect(i) = Del_Ind; + IMatrix(Del_Ind,:) = Inf; + IMatrix(:,Del_Ind) = Inf; + Need_Updata = find(Neightboor==Del_Ind); + L_Need=length(Need_Updata); + if L_Need>0 + [Values(Need_Updata),Neightboor(Need_Updata)]=min(IMatrix(Need_Updata,:),[],2); + end + Values(Del_Ind) = Inf; + + Zone(SortIndex(MDInd)).index = CandidateIndex; + Density(MDInd) = MDen - 1; + + end + SelInd(Have_Delect) = []; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Neighbor_Pairing_Strategy.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Neighbor_Pairing_Strategy.m new file mode 100644 index 000000000..66f0e299c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Neighbor_Pairing_Strategy.m @@ -0,0 +1,39 @@ +function [Mate1,Mate2,Mate3] = Neighbor_Pairing_Strategy(MatingPop,Zmin) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Jiawei Yuan + + Objs = MatingPop.objs; + [Num,M] = size(Objs); + Objs = (Objs - repmat(Zmin,Num,1)); + Objs = Objs./repmat(sqrt(sum(Objs.^2,2)),1,M); + + CosV = Objs * Objs'; + CosV = CosV - 3*eye(Num,Num); + + [~,SInd] = sort(-CosV,2); + + Nr = 10; + Neighbor = SInd(:,1:Nr); + + Mate1 = MatingPop; + + P = ones(Num,2); + for i = 1:Num + P(i,1:2) = Neighbor(i,randsample(Nr,2)); + if rand>0.7 + P(i,2) = randsample(Num,1); + end + end + + Mate2 = MatingPop(P(:,1)); + Mate3 = MatingPop(P(:,2)); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Selection_Operator_of_PREA.m b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Selection_Operator_of_PREA.m new file mode 100644 index 000000000..49af4b87b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/CMOQLMT/Selection_Operator_of_PREA.m @@ -0,0 +1,121 @@ +function NextIndex = Selection_Operator_of_PREA(PopObj,IMatrix,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Jiawei Yuan + + [~,M] = size(PopObj); + + % calculate the size of individuals in the first nondominant level + IrFitness = min(IMatrix,[],2); + Level1Index = find(IrFitness>=0); + Len_Level1 = length(Level1Index); + + if Len_Level1<=N + [~,SortIndex] = sort(-IrFitness); + NextIndex = SortIndex(1:N); + else + % only focus on the solutions in the first level + AllIndex = Level1Index; + PopObj = PopObj(Level1Index,:); + IMatrix = IMatrix(Level1Index,Level1Index); + + %% select the valuable solutions in the current population + MiddleIMatrix = IMatrix; + Ag1_n = Len_Level1 - N; + [Values,Neightboor] = min(MiddleIMatrix,[],2); + BestInd = 1:Len_Level1; + Have_Delect = zeros(1,Ag1_n); + + % mark the N solutions with the best fitness value by excluding the + % worst individuals one by one + for i=1:Ag1_n + [~,Del_Ind] = min(Values); + Have_Delect(i) = Del_Ind; + MiddleIMatrix(Del_Ind,:) = Inf; + MiddleIMatrix(:,Del_Ind) = Inf; + Need_Updata = find(Neightboor==Del_Ind); + L_Need=length(Need_Updata); + if L_Need>0 + [Values(Need_Updata),Neightboor(Need_Updata)]=min(MiddleIMatrix(Need_Updata,:),[],2); + end + Values(Del_Ind) = Inf; + end + BestInd(Have_Delect) = []; + + % determine the boundary of promising region + Zmax = max(PopObj(BestInd,:),[],1); + + % remove the individuals outside the promising region + OutIndex = find(min(repmat(Zmax,Len_Level1,1) - PopObj,[],2) < 0); + AllIndex(OutIndex) = []; + PopObj(OutIndex,:) = []; + IMatrix(OutIndex,:) = []; + IMatrix(:,OutIndex) = []; + Num = length(AllIndex); + + + %% diversity maintance mechanism based on parallel distance + % normalize the promising region + PopObj = PopObj./repmat(Zmax,Num,1); + + [Ir_Values,Ir_Neightboor] = min(IMatrix,[],2); + + DelectInd2 = []; + + DMatrix = zeros(Num,Num); + + % calculate the parallel distance matrix + for i = 1:Num + Fi = PopObj(i,:); + Fdelta = PopObj - repmat(Fi,Num,1); + DMatrix(i,:) = sqrt(sum(Fdelta.^2,2) - (sum(Fdelta,2)).^2./M); + DMatrix(i,i) = Inf; + end + + [Dis_Values,Dis_Neightboor] = min(DMatrix,[],2); + + + for l=1:(Num - N) + [~,individual1]=min(Dis_Values); + individual2=Dis_Neightboor(individual1); + + if Ir_Values(individual1)0 + [Dis_Values(Need_Updata_Dis),Dis_Neightboor(Need_Updata_Dis)]=min(DMatrix(Need_Updata_Dis,:),[],2); + end + Dis_Values(DelectInd2)= Inf; + + + + IMatrix(k,:) = Inf; + IMatrix(:,k) = Inf; + Need_Updata_Ir=find(Ir_Neightboor==k); + L_Need_Ir=length(Need_Updata_Ir); + if L_Need_Ir>0 + [Ir_Values(Need_Updata_Ir),Ir_Neightboor(Need_Updata_Ir)]=min(IMatrix(Need_Updata_Ir,:),[],2); + end + end + AllIndex(DelectInd2) = []; + + NextIndex = AllIndex; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CSEA/CSEA.m b/PlatEMO/Algorithms/Multi-objective optimization/CSEA/CSEA.m index 40ea6c3f6..9e82a0f77 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/CSEA/CSEA.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/CSEA/CSEA.m @@ -26,10 +26,30 @@ function main(Algorithm,Problem) [k,gmax] = Algorithm.ParameterSet(6,3000); %% Initalize the population by Latin hypercube sampling - N = 11*Problem.D-1; + N = min(11*Problem.D-1,109); PopDec = UniformPoint(N,Problem.D,'Latin'); Population = Problem.Evaluation(repmat(Problem.upper-Problem.lower,N,1).*PopDec+repmat(Problem.lower,N,1)); Arc = Population; + + %% Initialize the network + hiddenLayerSize = ceil(Problem.D*2); + layers = [featureInputLayer(Problem.D,'Normalization', 'zscore') + fullyConnectedLayer(hiddenLayerSize) + batchNormalizationLayer + reluLayer + fullyConnectedLayer(1) + sigmoidLayer + regressionLayer]; + + maxEpochs = 400; + miniBatchSize = 32; + options = trainingOptions('adam', ... + 'ExecutionEnvironment','auto', ... + 'MaxEpochs',maxEpochs, ... + 'MiniBatchSize',miniBatchSize, ... + 'Shuffle','every-epoch', ... + 'Plots','none', ... + 'Verbose',false);%, ... Plots','none' %% Optimization while Algorithm.NotTerminated(Arc) @@ -40,13 +60,10 @@ function main(Algorithm,Problem) rr = sum(Output)/length(Output); tr = min(rr,1-rr)*0.5; [TrainIn,TrainOut,TestIn,TestOut] = DataProcess(Input,Output); - - % Construct and update the FNN - net = FNN(ceil(Problem.D*2),800); - net.train(TrainIn,TrainOut); + net = trainNetwork(TrainIn,TrainOut-0,layers,options); % Error rates calculation - TestPre = net.predict(TestIn); + TestPre = predict(net,TestIn); IndexGood = TestOut==1; p0 = sum(abs((TestOut(IndexGood)-TestPre(IndexGood))))/sum(IndexGood); p1 = sum(abs((TestOut(~IndexGood)-TestPre(~IndexGood))))/sum(~IndexGood); diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CSEA/FNN.m b/PlatEMO/Algorithms/Multi-objective optimization/CSEA/FNN.m deleted file mode 100644 index 497883abe..000000000 --- a/PlatEMO/Algorithms/Multi-objective optimization/CSEA/FNN.m +++ /dev/null @@ -1,78 +0,0 @@ -classdef FNN < handle -% Feedforward neural network - -%------------------------------- Copyright -------------------------------- -% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for -% research purposes. All publications which use this platform or any code -% in the platform should acknowledge the use of "PlatEMO" and reference "Ye -% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform -% for evolutionary multi-objective optimization [educational forum], IEEE -% Computational Intelligence Magazine, 2017, 12(4): 73-87". -%-------------------------------------------------------------------------- - - properties(SetAccess = private) - nHidden = 0; % Size of the hidden layer - nIter = 0; % Number of iterations for each learning - WA = []; % Weights between the input layper and the hidden layer - WB = []; % Weights between the hidden layer and the output layer - end - methods - %% Constructor - function obj = FNN(nHidden,nIter) - obj.nHidden = nHidden; - obj.nIter = nIter; - end - - %% Train - function train(obj,X,T) - if isempty(obj.WA) || isempty(obj.WB) - obj.WA = randn(size(X,2)+1,obj.nHidden)./100; - obj.WB = randn(obj.nHidden+1,size(T,2))./100; - end - miu = 1; - k = 2; - for iter = 1 : obj.nIter - % Calculate the predictive output - [Z,Y] = obj.predict(X); - MSE = mean(mean((Z-T).^2)); - % Calculate the Jacobian matrix - J = zeros(size(X,1),numel(obj.WA)+numel(obj.WB)); - for i = 1 : size(X,1) - P = Z(i,:).*(1-Z(i,:)); - Q = P*obj.WB(2:end,:)'.*Y(i,:).*(1-Y(i,:)); - DB = [1,Y(i,:)]'*P; - DA = [1,X(i,:)]'*Q; - J(i,:) = [DA(:);DB(:)]; - end - % Update the value of each weight - J = round(J*1e4)/1e4; - while true - Delta = -(J'*J+miu*eye(size(J,2)))^-1*J'*sum(Z-T,2); - newWA = obj.WA + reshape(Delta(1:numel(obj.WA)),size(obj.WA)); - newWB = obj.WB + reshape(Delta(numel(obj.WA)+1:end),size(obj.WB)); - newY = 1./(1+exp(-[ones(size(X,1),1),X]*newWA)); - newZ = 1./(1+exp(-[ones(size(Y,1),1),newY]*newWB)); - if MSE < 1e-4 - return; - end - if mean(mean((newZ-T).^2)) < MSE - obj.WA = newWA; - obj.WB = newWB; - miu = miu/k; - break; - elseif miu > 1e4 - return; - else - miu = miu*k; - end - end - end - end - - %% Predict - function [Z,Y] = predict(obj,X) - Y = 1./(1+exp(-[ones(size(X,1),1),X]*obj.WA)); - Z = 1./(1+exp(-[ones(size(Y,1),1),Y]*obj.WB)); - end - end -end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/CSEA/SurrogateAssistedSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/CSEA/SurrogateAssistedSelection.m index 216519e0f..cfba1bb37 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/CSEA/SurrogateAssistedSelection.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/CSEA/SurrogateAssistedSelection.m @@ -13,7 +13,7 @@ % This function is written by Cheng He Next = OperatorGA(Problem,[Input;Ref.decs],{1,15,1,5}); - Label = net.predict(Next); + Label = predict(net,Next); a = tr; b = 1 - tr; i = 0; @@ -22,18 +22,19 @@ [~,index] = sort(Label,'descend'); Input = Next(index(1:length(Ref)),:); Next = OperatorGA(Problem,[Input;Ref.decs],{1,15,1,5}); - Label = net.predict(Next); + Label = predict(net,Next); i = i+size(Next,1); end Next = Next(Label>0.9,:); - elseif p0>b && p1b && p1 b while i CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/DPPPS.m b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/DPPPS.m new file mode 100644 index 000000000..9713e7453 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/DPPPS.m @@ -0,0 +1,170 @@ +classdef DPPPS< ALGORITHM +% +% Tri-population based push and pull search +% delta --- 0.9 --- The probability of choosing parents locally +% nr --- 2 --- Maximum number of solutions replaced by each offspring + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, L. Wang, and C. Lu, A tri-population based +% co-evolutionary framework for constrained multi-objective optimization +% problems, Swarm and Evolutionary Computation, 2022, 70: 101055. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + + %% Parameter setting + [delta,nr] = Algorithm.ParameterSet(0.9,2); + + %% Generate the weight vectors + [W,Problem.N] = UniformPoint(Problem.N,Problem.M); + T = ceil(Problem.N/10); + + %% Detect the neighbours of each solution + B = pdist2(W,W); + [~,B] = sort(B,2); + B = B(:,1:T); + + %% Generate random population + Population = Problem.Initialization(); + Z = min(Population.objs,[],1); + Population1 = Problem.Initialization(); + Population2 = Problem.Initialization(); + + alpha_c = 2./(1+exp(1).^(-Problem.FE*10/(3*Problem.maxFE)))-1; + para = ceil(Problem.maxFE/Problem.N)/2 - ceil(Problem.FE/(3*Problem.N)); + + %% Evaluate the Population + Tc = 0.9 * ceil(Problem.maxFE/Problem.N); + last_gen = 20; + change_threshold = 1e-1; + search_stage = 1; % 1 for push stage,otherwise,it is in pull stage. + max_change = 1; + epsilon_k = 0; + epsilon_0 = 0; + cp = 2; + alpha = 0.95; + tao = 0.05; + ideal_points = zeros(ceil(Problem.maxFE/Problem.N),Problem.M); + nadir_points = zeros(ceil(Problem.maxFE/Problem.N),Problem.M); + arch = archive(Population,Problem.N); + + %% Optimization + while Algorithm.NotTerminated(Population) + gen = ceil(Problem.FE/(2*Problem.N)); + pop_cons = Population.cons; + cv = overall_cv(pop_cons); + population = [Population.decs,Population.objs,cv]; + rf = sum(cv <= 1e-6) / Problem.N; + ideal_points(gen,:) = Z; + nadir_points(gen,:) = max(population(:,Problem.D + 1 : Problem.D + Problem.M),[],1); + + % The maximumrate of change of ideal and nadir points rk is calculated. + if gen >= last_gen + max_change = calc_maxchange(ideal_points,nadir_points,gen,last_gen); + end + + % The value of e(k) and the search strategy are set. + if gen < Tc + if max_change <= change_threshold && search_stage == 1 + search_stage = -1; + epsilon_0 = max(population(:,end),[],1); + epsilon_k = epsilon_0; + end + if search_stage == -1 + epsilon_k = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha,gen,Tc,cp); + end + else + epsilon_k = 0; + end + + % For each solution + for i = 1 : Problem.N + % Choose the parents + if rand < delta + P = B(i,randperm(size(B,2))); + else + P = randperm(Problem.N); + end + + % Generate an offspring + Offspring = OperatorDE(Problem,Population(i),Population(P(1)),Population(P(2))); + + % Update the ideal point + Z = min(Z,Offspring.obj); + + g_old = max(abs(Population(P).objs-repmat(Z,length(P),1)).*W(P,:),[],2); + g_new = max(repmat(abs(Offspring.obj-Z),length(P),1).*W(P,:),[],2); + cv_old = overall_cv(Population(P).cons); + cv_new = overall_cv(Offspring.con) * ones(length(P),1); + + if search_stage == 1 % Push Stage + Population(P(find(g_old>=g_new,nr))) = Offspring; + else % Pull Stage && An improved epsilon constraint-handling is employed to deal with constraints + Population(P(find(((g_old >= g_new) & (((cv_old <= epsilon_k) & (cv_new <= epsilon_k)) | (cv_old == cv_new)) | (cv_new < cv_old) ), nr))) = Offspring; + end + end + + % Re-rank + Population1 = Population1(randperm(Problem.N)); + Population2 = Population2(randperm(Problem.N)); + Lia = ismember(Population2.objs,Population1.objs, 'rows'); + gamma = 1-sum(Lia)/Problem.N; + [Population1,FrontNo1] = EnvironmentalSelection(Population1,Problem.N,alpha_c); + [Population2,FrontNo2] = EnvironmentalSelection_noCon(Population2,Problem.N,alpha_c,gamma,para); + + % Offspring Reproduction + Population_all = [Population1,Population2]; + RankSolution_all = [FrontNo1,FrontNo2]; + MatingPool = TournamentSelection(2,2*Problem.N,RankSolution_all); + Offspring = OperatorGAhalf(Problem,Population_all(MatingPool)); + + % Environmental Selection + alpha_c = 2./(1+exp(1).^(-Problem.FE*5/Problem.maxFE)) - 1; + para = ceil(Problem.maxFE/Problem.N)/2 - ceil(Problem.FE/(2*Problem.N)); + [Population1,~] = EnvironmentalSelection([Population1,Offspring],Problem.N,alpha_c); + [Population2,~] = EnvironmentalSelection_noCon([Population2,Offspring],Problem.N,alpha_c,gamma,para); + + % Output the non-dominated and feasible solutions. + arch = archive([arch,Population],Problem.N); + if Problem.FE >= Problem.maxFE + arch = archive([arch,Population1],Problem.N); + Population = arch; + end + end + end + end +end + +function result = overall_cv(cv) +% The Overall Constraint Violation + + cv(cv <= 0) = 0;cv = abs(cv); + result = sum(cv,2); +end + +function max_change = calc_maxchange(ideal_points,nadir_points,gen,last_gen) +% Calculate the Maximum Rate of Change + + delta_value = 1e-6 * ones(1,size(ideal_points,2)); + rz = abs((ideal_points(gen,:) - ideal_points(gen - last_gen + 1,:)) ./ max(ideal_points(gen - last_gen + 1,:),delta_value)); + nrz = abs((nadir_points(gen,:) - nadir_points(gen - last_gen + 1,:)) ./ max(nadir_points(gen - last_gen + 1,:),delta_value)); + max_change = max([rz, nrz]); +end + +function result = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha,gen,Tc,cp) + if rf < alpha + result = (1 - tao) * epsilon_k; + else + result = epsilon_0 * ((1 - (gen / Tc)) ^ cp); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection.m new file mode 100644 index 000000000..bbb9b8032 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection.m @@ -0,0 +1,122 @@ +function [Population,RankSolution] = EnvironmentalSelection(Population,N,alpha) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Mengjun Ming + + %% Parameter + popSize = length(Population); + NumSeq = (1:popSize)'; + RankConvg = zeros(popSize,1); + RankDivs = zeros(popSize,1); + + %% Modify the infeasible solutions + PopObj = Population.objs; + PopCon = Population.cons; + z = min(PopObj,[],1); + n = max(PopObj,[],1); + M = length(z); + [W,~] = UniformPoint(N,M); + [~,Region] = min(pdist2(PopObj-z,W,'cosine'),[],2); + PopObj_2 = PopObj; + Infeasible_all = any(PopCon>0,2); + if sum(Infeasible_all) ~= 0 + [~,Region_Fmax] = min(pdist2(n-z,W,'cosine'),[],2); + PopObj_2(Infeasible_all,:) = repmat(n,sum(Infeasible_all),1)+sum(max(0,PopCon(Infeasible_all,:)),2)*W(Region_Fmax,:)/norm(W(Region_Fmax,:)); + end + + %% Non-dominated sorting + CV = sum(max(0,PopCon),2); + Dominate = false(popSize); + for i = 1 : popSize-1 + for j = i+1 : popSize + if CV(i) < CV(j) + Dominate(i,j) = true; + elseif CV(i) > CV(j) + Dominate(j,i) = true; + else + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,popSize); + for i = 1 : popSize + R(i) = sum(S(Dominate(:,i))); + end + FrontNo = R + 1; + + %% Calculate the crowding distance of each solution + PopObj = Population.objs; + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + CrowdDis = Distance(:,floor(sqrt(popSize))); + + %% Add a middle column + MiddleLevel = zeros(popSize,1); + + %% Environmental selection -- convergence + Next = FrontNo == 1; + if sum(Next) <= N + [~,indx_Convg] = sortrows([FrontNo',-CrowdDis]); + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + MiddleLevel(Temp(Del)) = 1; + [~,indx_Convg] = sortrows([FrontNo',MiddleLevel,-CrowdDis]); + end + RankConvg(indx_Convg) = NumSeq; + + %% Environmental selection -- diversity + FrontNo_D = ones(popSize,1); + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + g_temp = sum((Objs_temp-z).*W(i,:),2); + [~,index_FrontNo_D] = sort(g_temp); + FrontNo_D(index(index_FrontNo_D)) = (1:length(g_temp))'; + end + end + FrontNo_D = FrontNo_D+Infeasible_all*popSize; + [~,indx_divs] = sortrows([FrontNo_D,-CrowdDis]); + RankDivs(indx_divs) = NumSeq; + + %% Population for next generation + RankSolution = alpha*RankConvg+(1-alpha)*RankDivs; + [~,Rank] = sort(RankSolution); + Population = Population(Rank(1:N)); + RankSolution = 1 : N; +end +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection_noCon.m b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection_noCon.m new file mode 100644 index 000000000..c975b45c1 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/EnvironmentalSelection_noCon.m @@ -0,0 +1,126 @@ +function [Population,RankSolution] = EnvironmentalSelection_noCon(Population,N,alpha,gamma,para) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Mengjun Ming + + %% Parameter + popSize = length(Population); + NumSeq = (1:popSize)'; + RankConvg = zeros(popSize,1); + RankDivs = zeros(popSize,1); + + %% Modify the infeasible solutions + PopObj = Population.objs; + PopCon = Population.cons; + z = min(PopObj,[],1); + n = max(PopObj,[],1); + + Infeasible_all = any(PopCon>0,2); + phi_max = max(sum(max(0,PopCon(Infeasible_all,:)),2)); + + M = length(z); + [W,~] = UniformPoint(N,M); + [~,Region] = min(pdist2(PopObj-z,W,'cosine'),[],2); + PopObj_2 = PopObj; + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + Cons_temp = PopCon(index,:); + Infeasible = any(Cons_temp>0,2); + if (sum(Infeasible)~=0) + F_max = max(Objs_temp,[],1); + PopObj_2(index(Infeasible),:) = Objs_temp(Infeasible,:)+(sum(max(0,Cons_temp(Infeasible,:)),2)/phi_max).^(exp(para)/max(gamma,0.000001)).*(F_max-Objs_temp(Infeasible,:)); + end + end + end + + %% Non-dominated sorting + Dominate = false(popSize); + for i = 1 : popSize-1 + for j = i+1 : popSize + k = any(PopObj_2(i,:)PopObj_2(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,popSize); + for i = 1 : popSize + R(i) = sum(S(Dominate(:,i))); + end + FrontNo = R + 1; + + %% Calculate the crowding distance of each solution + PopObj = Population.objs; + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + CrowdDis = Distance(:,floor(sqrt(popSize))); + + %% Add a middle column + MiddleLevel = zeros(popSize,1); + + %% Environmental selection + Next = FrontNo == 1; + if sum(Next) <= N + [~,indx_Convg] = sortrows([FrontNo',-CrowdDis]); + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + MiddleLevel(Temp(Del)) = 1; + [~,indx_Convg] = sortrows([FrontNo',MiddleLevel,-CrowdDis]); + end + RankConvg(indx_Convg) = NumSeq; + + %% Environmental selection -- diversity + FrontNo_D = ones(popSize,1); + for i = 1:size(W,1) + index = find(Region==i); + if (~isempty(index)) + Objs_temp = PopObj_2(index,:); + g_temp = sum((Objs_temp-z).*W(i,:),2); + [~,index_FrontNo_D] = sort(g_temp); + FrontNo_D(index(index_FrontNo_D)) = (1:length(g_temp))'; + end + end + [~,indx_divs] = sortrows([FrontNo_D,-CrowdDis]); + RankDivs(indx_divs) = NumSeq; + + %% Population for next generation + RankSolution = alpha*RankConvg+(1-alpha)*RankDivs; + [~,Rank] = sort(RankSolution); + Population = Population(Rank(1:N)); + RankSolution = 1 : N; +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/archive.m b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/archive.m new file mode 100644 index 000000000..8eeecdd24 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/DP-PPS/archive.m @@ -0,0 +1,44 @@ +function Population = archive(Population,N) +% Select feasible and non-dominated solutions by using NSGA-II + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + %% Select feasible solutions + fIndex = all(Population.cons <= 0,2); + Population = Population(fIndex); + + if isempty(Population) + return + elseif size(Population,2) > N + Fitness = CalFitness(Population.objs,Population.cons); + Next = Fitness < 1; + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + Population = Population(Next); + end +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Divide.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Divide.m new file mode 100644 index 000000000..0697207eb --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Divide.m @@ -0,0 +1,29 @@ +function [Population1,Mask1,Dec1,FrontNo1,SV1,Population2,Mask2,Dec2,FrontNo2,SV2] = Divide(Population1,Mask1,Dec1,FrontNo1,row,col) +% The divide operation of HHC-MMEA +%The Divisionflag() function returns the sequence number to perform this split + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + % subpopulation2 + Population2 = Population1(col); + Dec2 = Dec1(col,:); + Mask2 = Mask1(col,:); + FrontNo2 = FrontNo1(col); + mask = Mask2(FrontNo2==1,:); + SV2 = sum(mask,1)/size(mask,1); + + %subpopulation1 + Population1 = Population1(row); + Dec1 = Dec1(row,:); + Mask1= Mask1(row,:); + FrontNo1 = FrontNo1(row); + mask = Mask1(FrontNo1==1,:); + SV1 = sum(mask,1)/size(mask,1); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/DivisionFlag.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/DivisionFlag.m new file mode 100644 index 000000000..e334a05ca --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/DivisionFlag.m @@ -0,0 +1,42 @@ +function [subp1,subp2,DivisionFlag] = DivisionFlag(Mask,FrontNo) +% Calculate the farthest two solutions and their similarity + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + mask = Mask(FrontNo==1,:); + dis = pdist2(mask,mask,'hamming'); + M = max(max(dis));%maximum distance + [leader1,leader2] = find(dis==M,1); + [~,subleader1]=ismember(mask(leader1,:),Mask,'rows'); + [~,subleader2]=ismember(mask(leader2,:),Mask,'rows'); + subp1 = subleader1; + subp2 = subleader2; + s = sum(mask(leader1,:)&mask(leader2,:))/min(sum(mask(leader1,:)),sum(mask(leader2,:)));%相似度 + if M>0.05 && s<=0.5 + DivisionFlag = 1; + %% hierarchical clustering(divide operation) + for i = 1:size(Mask,1) + if i~=subleader1 && i~=subleader2 + dis1 = pdist2(mask(leader1,:),Mask(i,:),'hamming'); + dis2 = pdist2(mask(leader2,:),Mask(i,:),'hamming'); + if dis1 <= dis2 + subp1 = [subp1,i]; + else + subp2 = [subp2,i]; + end + end + end + else + DivisionFlag = 0; + end + if size(Mask,1) <50 + DivisionFlag = 0; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/EnvironmentalSelection.m new file mode 100644 index 000000000..78a2c1ec5 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/EnvironmentalSelection.m @@ -0,0 +1,38 @@ +function [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection(Population,Dec,Mask,N,dis) +% The environmental selection of HHC-MMEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + if nargin > 4 + PopObj = [Population.objs,dis]; + else + PopObj = Population.objs; + end + N = min(N,length(Population)); + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(PopObj,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(PopObj,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); + Dec = Dec(Next,:); + Mask = Mask(Next,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/HHCMMEA.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/HHCMMEA.m new file mode 100644 index 000000000..9f4ecba15 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/HHCMMEA.m @@ -0,0 +1,186 @@ +classdef HHCMMEA < ALGORITHM +% +% Hybrid hierarchical clustering based multi-modal multi-objective evolutionary algorithm + +%------------------------------- Reference -------------------------------- +% Z. Ding, L. Cao, L. Chen, D. Sun, X. Zhang, and Z. Tao, Large-scale +% multimodal multiobjective evolutionary optimization based on hybrid +% hierarchical clustering, Knowledge-Based Systems, 2023, 266: 110398. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +%This function is written by Lve Cao +%Fitness vector is the global guide vector and GV is the local guide vector +%in the original paper; + + methods + function main(Algorithm,Problem) + %% Calculate the fitness of each decision variable + Fitness = zeros(1,Problem.D); + REAL = ~strcmp(Problem.encoding,'binary'); + for i = 1 : 1+4*REAL + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.D,1),repmat(Problem.upper,Problem.D,1)); + else + Dec = ones(Problem.D,Problem.D); + end + Mask = eye(Problem.D); + Population = Problem.Evaluation(Dec.*Mask); + Fitness = Fitness + NDSort([Population.objs,Population.cons],inf); + end + %% Population initialization + P = UniformPoint(Problem.N,Problem.D,'Latin'); + Dec = P.*repmat(Problem.upper-Problem.lower,Problem.N,1) + repmat(Problem.lower,Problem.N,1); + Mask = double(UniformPoint(Problem.N,Problem.D,'Latin') > 0.5); + Population = Problem.Evaluation(Dec.*Mask); + K = 1; % Number of subpopulations + Masks = cell(1,K); + Decs = cell(1,K); + Populations = cell(1,K); + GV = cell(1,K); + FrontNo = cell(1,K); + CrowdDis = cell(1,K); + leader = cell(1,K); + index = randperm(floor(Problem.N/K)*K); + temp = reshape(index,K,floor(Problem.N/K)); + for i = 1 : K + Populations{i} = Population(temp(i,:)); + Masks{i} = Mask(temp(i,:),:); + Decs{i} = Dec(temp(i,:),:); + [Populations{i},Decs{i},Masks{i},FrontNo{i},CrowdDis{i}] = EnvironmentalSelection(Populations{i},Decs{i},Masks{i},length(Populations{i})); + GV{i} = UpdateGV(zeros(1,Problem.D),Masks{i},FrontNo{i}); + end + %% Optimization + while Algorithm.NotTerminated(Population) + [~,rank] = sort(SubPopRank(Populations)); + for i = 1 : K + GV{rank(i)} = UpdateGV(GV{rank(i)},Masks{rank(i)},FrontNo{rank(i)}); + Mating = TournamentSelection(2,2*length(Populations{rank(i)}),FrontNo{rank(i)},-CrowdDis{rank(i)}); + % Adaptive Variation + fgv = GV{rank(i)}; + fgv(fgv<1e-2) = 0; + idx = kmeans(fgv',2); + idx = idx'; + s1 = sum(GV{rank(i)}(idx==1))/sum(idx==1); + s2 = sum(GV{rank(i)}(idx==2))/sum(idx==2); + s = max(s1,s2); + delta= Problem.FE/Problem.maxFE; + if s < 0.5 || K==1 + [OffDec,OffMask] = Operator1(Problem,Decs{rank(i)}(Mating,:),Masks{rank(i)}(Mating,:),Fitness); + else + leader{rank(i)} = Leader(Masks{rank(i)},FrontNo{rank(i)},GV{rank(i)},Problem.D); + [OffDec,OffMask] = Operator2(Problem,Decs{rank(i)}(Mating,:),Masks{rank(i)}(Mating,:),GV{rank(i)},delta); + end + Offspring = Problem.Evaluation(OffDec.*OffMask); + Populations{rank(i)} = [Populations{rank(i)},Offspring]; + Decs{rank(i)} = [Decs{rank(i)};OffDec]; + Masks{rank(i)} = [Masks{rank(i)};OffMask]; + + % Improve Environmental Selection + if K == 1 + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},CrowdDis{rank(i)}] = EnvironmentalSelection(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},floor(length(Populations{rank(i)})/2)); + else + r = zeros(1,Problem.D); + for j = 1:K + if j~=i + fgv = GV{rank(j)}; + fgv(fgv < 1e-2) = 0; + idx = kmeans(fgv',2); + idx = idx'; + s1 = sum(fgv(idx==1))/sum(idx==1); + s2 = sum(fgv(idx==2))/sum(idx==2); + s = max(s1,s2); + if s > 0.5 + if s1 > s2 + r(idx==1) = r(idx==1)+ fgv(idx==1); + else + r(idx==2) = r(idx==2)+ fgv(idx==2); + end + end + end + end + r(r>0)=1; + dis = sum(repmat(r,length(Populations{rank(i)}),1)&Masks{rank(i)},2); + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},CrowdDis{rank(i)}] = EnvironmentalSelection(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},floor(length(Populations{rank(i)})/2),dis); + end + end + + %% hybrid hierarchical clustering + if mod(ceil(Problem.FE/Problem.N),10)==0 + k = K; + %divide operation(Line103-118) + while 1 + for i = 1 : K + [row,col,divisionFlag] = DivisionFlag(Masks{i},FrontNo{i}); + if divisionFlag == 1 + k = k+1; + [Populations{i},Masks{i},Decs{i},FrontNo{i},GV{i},Populations{k},Masks{k},Decs{k},FrontNo{k},GV{k}] = Divide(Populations{i},Masks{i},Decs{i},FrontNo{i},row,col); + leader{i}= Masks{i}(1,:); + leader{k}= Masks{k}(1,:); + end + end + K = k; + if divisionFlag == 0 + break; + end + end + % merge operation(Line120-143) + fgv = GV; + for i = 1: K + fgv{i}(fgv{i} < 0.5) = 0 ; + fgv{i}(fgv{i} > 0) = 1; + mask = Masks{i}(FrontNo{i}==1,:); + dis = pdist2(mask,fgv{i},'hamming'); + index = find(dis==min(dis),1); + fgv{i} = mask(index,:); + end + [ss,index] = SubPopSimility(Populations,leader); + while K>2 && ss>0.5 + i = index(1); + j = index(2); + [Populations{i},Decs{i},Masks{i},FrontNo{i},CrowdDis{i}] = EnvironmentalSelection([Populations{i},Populations{j}],[Decs{i};Decs{j}],[Masks{i};Masks{j}],length(Populations{i})); + Populations(j) = []; + Decs(j) = []; + Masks(j) = []; + FrontNo(j) = []; + GV(j) = []; + fgv(j) = []; + K = K-1; + [ss,index] = SubPopSimility(Populations(1:K),Masks); + end + % fill operation + popsize = floor(Problem.N/K); %average size of each Subpopulation + [~,rank] = sort(SubPopRank(Populations)); + for i = 1:K + len = popsize - length(Populations{rank(i)}); + if len > 0 + P = UniformPoint(len,Problem.D,'Latin'); + dec = P.*unifrnd(repmat(Problem.lower,len,1),repmat(Problem.upper,len,1)); + mask = zeros(len,Problem.D); + F = zeros(1,Problem.D); + for j = 1: K + if j ~=i + F = F + GV{rank(j)}; + end + end + for j = 1 : len + mask(j,TournamentSelection(2,floor(rand*Problem.D),F)) = 1; + end + Populations{rank(i)} = [Populations{rank(i)},Problem.Evaluation(dec.*mask)]; + Masks{rank(i)} = [Masks{rank(i)};mask]; + Decs{rank(i)} = [Decs{rank(i)};dec]; + end + [Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},FrontNo{rank(i)},CrowdDis{rank(i)}] = EnvironmentalSelection(Populations{rank(i)},Decs{rank(i)},Masks{rank(i)},popsize); + end + end + Population = [Populations{:}]; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Leader.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Leader.m new file mode 100644 index 000000000..6a64a0000 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Leader.m @@ -0,0 +1,20 @@ +function leader = Leader(Mask,Front,GV,D) +% update the leader by GV + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + fs= zeros(1,D); + sample = rand(1,D); + fs(GV>sample) = 1; + mask = Mask(Front==1,:); + dis = pdist2(mask,fs,'hamming'); + index = find(dis==min(dis),1); + leader = mask(index,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator1.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator1.m new file mode 100644 index 000000000..c937e8303 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator1.m @@ -0,0 +1,63 @@ +function [OffDec,OffMask] = Operator1(Problem, ParentDec,ParentMask,Fitness) +% The operator of SparseEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Parameter setting + [N,D] = size(ParentDec); + Parent1Mask = ParentMask(1:N/2,:); + Parent2Mask = ParentMask(N/2+1:end,:); + + %% Crossover for mask + OffMask = Parent1Mask; + for i = 1 : N/2 + if rand < 0.5 + index = find(Parent1Mask(i,:)&~Parent2Mask(i,:)); + index = index(TS(-Fitness(index))); + OffMask(i,index) = 0; + else + index = find(~Parent1Mask(i,:)&Parent2Mask(i,:)); + index = index(TS(Fitness(index))); + OffMask(i,index) = Parent2Mask(i,index); + end + end + + + %% Mutation for mask + for i = 1 : N/2 + if rand < 0.5 + index = find(OffMask(i,:)); + index = index(TS(-Fitness(index))); + OffMask(i,index) = 0; + else + index = find(~OffMask(i,:)); + index = index(TS(Fitness(index))); + OffMask(i,index) = 1; + end + end + + %% Crossover and mutation for dec + if any(Problem.encoding~=4) + OffDec = OperatorGAhalf(Problem,ParentDec); + OffDec(:,Problem.encoding==4) = 1; + else + OffDec = ones(N/2,D); + end +end + +function index = TS(Fitness) +% Binary tournament selection + + if isempty(Fitness) + index = []; + else + index = TournamentSelection(2,1,Fitness); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator2.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator2.m new file mode 100644 index 000000000..66799a78a --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/Operator2.m @@ -0,0 +1,74 @@ +function [OffDec,OffMask] = Operator2(Problem,ParentDec,ParentMask,Score, delta) +% Operator of HHC-MMEA + +%-------------------------------------------------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB Platform +% for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Parameter setting + N = size(ParentDec,1); + Parent1Mask = ParentMask(1:N/2,:); + Parent2Mask = ParentMask(N/2+1:end,:); + + %% Crossover for mask + OffMask = Parent1Mask; + for i = 1 : N/2 + index1 = find(Parent1Mask(i,:)&~Parent2Mask(i,:)); + index2 = find(~Parent1Mask(i,:)&Parent2Mask(i,:)); + p1 = 1./(1+exp(-Score(index1))); + p2 = 1./(1+exp(-Score(index2))); + idx1 = index1(p1rand(size(p2))); + OffMask(i,idx1) = 0; + OffMask(i,idx2) = 1; + end + + %% Mutation for mask + if rand<(1-delta) + for i = 1 : N/2 + if rand < 0.5 + index = find(OffMask(i,:)); + index = index(TS(Score(index))); + OffMask(i,index) = 0; + else + index = find(~OffMask(i,:)); + index = index(TS(-Score(index))); + OffMask(i,index) = 1; + end + end + else + [N,D] = size(ParentDec); + Mutation_p=1/D; % Probability of mutation + Mu_exchange=rand(N/2,D) ss + ss = simility(fs{i}(1,:),fs{j}(1,:)); + index = [i,j]; + end + end + end +end + +function s = simility(subPop1,subPop2) + s = sum(subPop1&subPop2)/min(sum(subPop1),sum(subPop2)); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/UpdateGV.m b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/UpdateGV.m new file mode 100644 index 000000000..bbfb6d7c9 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/HHC-MMEA/UpdateGV.m @@ -0,0 +1,30 @@ +function gv = UpdateGV(gv,Mask,FrontNo) +% Update the guiding vectors + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + Mask = Mask(FrontNo==1,:); + k = ceil(0.1*size(Mask,1)); + v = zeros(1,size(Mask,2)); + for i = 1 : size(Mask,1) + rand_Mask = Mask(i,:); + dis = pdist2(rand_Mask,Mask,'hamming'); + [~,index] = sort(dis); + knearest_Mask = Mask(index(1:k),:); + rand_Mask(rand_Mask==1) = 0.5 ; + knearest_Mask(knearest_Mask==1) = 0.5 ; + v = v + sum(repmat(rand_Mask,k,1)+knearest_Mask,1)/(size(Mask,1)*k); + end + if all(gv==0) + gv = v; + else + gv = 0.9*gv + 0.1*v; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-D-EGO/EGOSelect.m b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-D-EGO/EGOSelect.m index 227f65e3c..47873eb76 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/MOEA-D-EGO/EGOSelect.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/MOEA-D-EGO/EGOSelect.m @@ -99,7 +99,7 @@ end function [y,x] = GPcal(lamda,mu,sig2) -% Calculate the mu (x) and sigma^2 (y) of the aggregation function +% Calculate the mu (y) and sigma^2 (x) of the aggregation function tao = sqrt(lamda(1)^2*sig2(1)+lamda(2)^2*sig2(2)); alpha = (mu(1)-mu(2))/tao; diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/CalFitnessPC.m b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/CalFitnessPC.m new file mode 100644 index 000000000..9af8ab70c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/CalFitnessPC.m @@ -0,0 +1,75 @@ +function [Input,Output,Pa,Pmid] = CalFitnessPC(PopObj,PopDec,rate) +% Calculate the fitness value by using a new way considering +% convergence and the diversity + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + N = size(PopObj,1); + Zmin = min(PopObj,[],1); + Zmax = max(PopObj); + PopObj = (PopObj-repmat(Zmin,N,1))./(repmat(Zmax,N,1)-repmat(Zmin,N,1)); + Objs = PopObj; + SDE = zeros(N,1); + for i = 1 : N + SPopuObj = PopObj; + Temp = repmat(PopObj(i,:),N,1); + Shifted = PopObj < Temp; + SPopuObj(Shifted) = Temp(Shifted); + Distance = pdist2(real(PopObj(i,:)),real(SPopuObj)); + [~,index] = sort(Distance,2); + Dk = Distance(index(floor(sqrt(N))+1)); % Dk denotes the distance of solution i and its floor(sqrt(N)+1)-th nearest neighbour + SDE(i)=2./(Dk+2); + end + PopObj = SDE; + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(real(Objs),real(Objs),'cosine'); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 2./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Rmin = min(R); + Rmax = max(R); + R = (R-repmat(Rmin,1,N))./(repmat(Rmax,1,N)-repmat(Rmin,1,N)); + R = R'; + Fitness = rate*R + (1-rate)*D*2; + + [~, index] = sort(Fitness); + Input = [PopDec(index(1:ceil(N/4)),:);PopDec(index(end-(ceil(N/2)-ceil(N/4))+1:end),:)]; + Input = [Input,[Fitness(index(1:ceil(N/4)));Fitness(index(end-(ceil(N/2)-ceil(N/4))+1:end))]]; + Output = zeros(ceil(N/2),1); + Output(1:ceil(N/4)) = 2; + Output(ceil(N/4)+1:end) = 1; + Pa = Input(1:ceil(N/4),:); + Pmid = Input(ceil(N/4)-floor(N/8):ceil(N/4)+floor(N/8),:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/DataProcess.m b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/DataProcess.m new file mode 100644 index 000000000..ea044929a --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/DataProcess.m @@ -0,0 +1,25 @@ +function [TrainIn,TrainOut,TestIn,TestOut] = DataProcess(Input,Output) +% Divide the data into the train data and test data in proportion + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + + index1 = find(Output>1); + index0 = find(Output<=1); + K1 = false(1,length(index1)); + K0 = false(1,length(index0)); + K1(randperm(length(index1),ceil(3/4*length(index1)))) = true; + K0(randperm(length(index0),ceil(3/4*length(index0)))) = true; + K = [index1(K1);index0(K0)]; + TrainIn = Input(K,:); + TrainOut = Output(K); + TestIn = Input(setdiff(1:size(Input,1),K),:); + TestOut = Output(setdiff(1:size(Input,1),K)); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/ESCalFitness.m b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/ESCalFitness.m new file mode 100644 index 000000000..29100b42f --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/ESCalFitness.m @@ -0,0 +1,45 @@ +function Fitness = ESCalFitness(PopObj) +% Calculate the fitness of each solution + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + N = size(PopObj,1); + + %% Detect the dominance relation between each two solutions + Dominate = false(N); + for i = 1 : N-1 + for j = i+1 : N + k = any(PopObj(i,:)PopObj(j,:)); + if k == 1 + Dominate(i,j) = true; + elseif k == -1 + Dominate(j,i) = true; + end + end + end + + %% Calculate S(i) + S = sum(Dominate,2); + + %% Calculate R(i) + R = zeros(1,N); + for i = 1 : N + R(i) = sum(S(Dominate(:,i))); + end + + %% Calculate D(i) + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Distance = sort(Distance,2); + D = 1./(Distance(:,floor(sqrt(N)))+2); + + %% Calculate the fitnesses + Fitness = R + D'; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/EnvironmentalSelection.m new file mode 100644 index 000000000..d63d1797c --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/EnvironmentalSelection.m @@ -0,0 +1,44 @@ +function [Population,Fitness] = EnvironmentalSelection(Population,N) +% The environmental selection of SPEA2 + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Calculate the fitness of each solution + Fitness = ESCalFitness(Population.objs); + + %% Environmental selection + Next = Fitness < 1; + if sum(Next) < N + [~,Rank] = sort(Fitness); + Next(Rank(1:N)) = true; + elseif sum(Next) > N + Del = Truncation(Population(Next).objs,sum(Next)-N); + Temp = find(Next); + Next(Temp(Del)) = false; + end + % Population for next generation + Population = Population(Next); + Fitness = Fitness(Next); +end + +function Del = Truncation(PopObj,K) +% Select part of the solutions by truncation + + %% Truncation + Distance = pdist2(PopObj,PopObj); + Distance(logical(eye(length(Distance)))) = inf; + Del = false(1,size(PopObj,1)); + while sum(Del) < K + Remain = find(~Del); + Temp = sort(Distance(Remain,Remain),2); + [~,Rank] = sortrows(Temp); + Del(Remain(Rank(1))) = true; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/PCSAEA.m b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/PCSAEA.m new file mode 100644 index 000000000..ccf22d733 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/PCSAEA.m @@ -0,0 +1,60 @@ +classdef PCSAEA < ALGORITHM +% +% Pairwise comparison based surrogate-assisted evolutionary algorithm +% delta --- 0.8 --- Threshold of reliability measurement +% gmax --- 3000 --- Number of solutions evaluated by surrogate model + +%------------------------------- Reference -------------------------------- +% Y. Tian, J. Hu, C. He, H. Ma, L. Zhang, and X. Zhang, A pairwise +% comparison based surrogate-assisted evolutionary algorithm for expensive +% multi-objective optimization, Swarm and Evolutionary Computation, 2023. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + methods + function main(Algorithm,Problem) + %% Parameter setting + [delta,gmax] = Algorithm.ParameterSet(0.8,3000); + + %% Initalize the population by Latin hypercube sampling + N = max(11*Problem.D-1,Problem.N); + PopDec = UniformPoint(N,Problem.D,'Latin'); + Population = Problem.Evaluation(repmat(Problem.upper-Problem.lower,N,1).*PopDec+repmat(Problem.lower,N,1)); + Arc = Population; + t = 1; + + %% Optimization + while Algorithm.NotTerminated(Arc) + % Select a balance sample set by a new fitness + [Input,Output,Pa,Pmid] = CalFitnessPC(Population.objs,Population.decs,(Problem.FE/Problem.maxFE)); + % Data process + [TrainIn,~,TestIn,TestOut] = DataProcess(Input,Output); + % Construct and update the FNN£¬global classify surrogate model + net = RBFNNPC(0.1925); + net.train(TrainIn,Problem.D); + + % Error rates calculation + TestPre = net.lastpredict(TestIn,Problem.D,Pmid,1); + % New and suitble reliability selection + validIndex = TestPre~=1.5; + Error1 = sum(TestOut(validIndex)==TestPre(validIndex))/length(TestOut); + Error2 = sum(TestOut(validIndex)~=TestPre(validIndex))/length(TestOut); + + % Surrogate-assisted selection and update the population + Next = SurrogateAssistedSelectionPC(Problem,net,Error1,Error2,Population.decs,gmax,Pa,Problem.D,0,delta); + if ~isempty(Next) + Arc = [Arc,Problem.Evaluation(Next)]; + end + + Population = EnvironmentalSelection(Arc,Problem.N); + t = t+1; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/RBFNNPC.m b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/RBFNNPC.m new file mode 100644 index 000000000..68934c336 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/PC-SAEA/RBFNNPC.m @@ -0,0 +1,84 @@ +classdef RBFNNPC < handle +% Radial Basis Network + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + properties(SetAccess = private) + spread = 0; + srgtSRGT = cell(1,1); + end + methods + %% Constructor + function obj = RBFNNPC(spread) + obj.spread = spread; + end + + %% Train + function train(obj,X,distanceD) + % Input ---(x1£¬x2) £¬decision number is changed to 2*D + % Output --1 or 0 , meaning is better or worse + N = size(X,1); + input = zeros(N*N,2*distanceD); + output = zeros(N*N,1); + FrontNo = X(:,end); + for i = 1:N + for j = 1:N + input(N*(i-1)+j,:) = [X(i,1:distanceD),X(j,1:distanceD)]; + if FrontNo(i)= 0.95) == 0) || (sum(GoodLabel >= 0.95) > floor(lnum/2)) + [~,index] = sort(GoodLabel(:,end),'descend'); + Next = GoodNext(index(1:floor(lnum/2)),1:D); + else + Next = GoodNext(GoodLabel >= 0.95,:); + end + + elseif error2 < 1-delta + while i <= wmax + [~,index] = sort(Label); + GoodNext(i,:) = Next(index(1),:); + GoodLabel(i,:) = Label(index(1)); + Input = Next(index(lnum),:); + Parent = [Input;Pa]; + Parent = Parent(randperm(size(Parent,1)),:); + Next = OperatorGA(Problem,Parent,{1,15,1,5}); + Label = net.lastpredict(Next,D,Pa,flag); + i = i+1; + end + if (sum(GoodLabel <= -0.95) == 0) || (sum(GoodLabel <= -0.95) > floor(lnum/2)) + [~,index] = sort(GoodLabel(:,end)); + Next = GoodNext(index(1:floor(lnum/2)),1:D); + else + Next = GoodNext(GoodLabel <= -0.95,:); + end + else + Next = Next(randi(end),:); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/POCEA/POCEA.m b/PlatEMO/Algorithms/Multi-objective optimization/POCEA/POCEA.m index 97c5d3537..191ea9742 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/POCEA/POCEA.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/POCEA/POCEA.m @@ -64,5 +64,5 @@ function main(Algorithm,Problem) DIS = sum(PopObj.^2,2); THETA = acos(1 - pdist2(PopObj,V,'cosine')); [~,index] = sort(THETA,1); - INDEX = index(1:min(k,length(index)),:); + INDEX = index(1:min(k,end),:); end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/PPS/PPS.m b/PlatEMO/Algorithms/Multi-objective optimization/PPS/PPS.m index e2fee892b..5516f5b8c 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/PPS/PPS.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/PPS/PPS.m @@ -75,7 +75,7 @@ function main(Algorithm,Problem) epsilon_k = epsilon_0; end if search_stage == -1 - epsilon_k = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha,gen,Tc,cp); + epsilon_k = update_epsilon(tao,epsilon_k,epsilon_0,rf,alpha,gen,Tc,cp); end else epsilon_k = 0; diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnSelection.m new file mode 100644 index 000000000..ec7ce064f --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnSelection.m @@ -0,0 +1,28 @@ +function [Population,FrontNo,CrowdDis] = EnSelection(Population,N) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(Population.objs,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnvironmentalSelection.m new file mode 100644 index 000000000..7cf4ee5b4 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/EnvironmentalSelection.m @@ -0,0 +1,38 @@ +function [Population,Dec,Mask,FrontNo,CrowdDis] = EnvironmentalSelection(Population,Dec,Mask,N) +% The environmental selection of SparseEA + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Delete duplicated solutions + [~,uni] = unique(Population.objs,'rows'); + Population = Population(uni); + Dec = Dec(uni,:); + Mask = Mask(uni,:); + N = min(N,length(Population)); + + %% Non-dominated sorting + [FrontNo,MaxFNo] = NDSort(Population.objs,Population.cons,N); + Next = FrontNo < MaxFNo; + + %% Calculate the crowding distance of each solution + CrowdDis = CrowdingDistance(Population.objs,FrontNo); + + %% Select the solutions in the last front based on their crowding distances + Last = find(FrontNo==MaxFNo); + [~,Rank] = sort(CrowdDis(Last),'descend'); + Next(Last(Rank(1:N-sum(Next)))) = true; + + %% Population for next generation + Population = Population(Next); + FrontNo = FrontNo(Next); + CrowdDis = CrowdDis(Next); + Dec = Dec(Next,:); + Mask = Mask(Next,:); +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/InitRank.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/InitRank.m new file mode 100644 index 000000000..ee4be28db --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/InitRank.m @@ -0,0 +1,60 @@ +function [SubPopulation,SubDec,SubMask,Rank] = InitRank(SubPopulation,SubDec,SubMask,Problem) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + Rank = {}; + SubPopulationCount = zeros(1,size(SubPopulation,2)); + for i = 1 : size(SubPopulation,2) + SubPopulationCount(i) = size(SubPopulation{i},2); + end + for j = 1 : size(SubPopulation,2) + [~,FrontNo{j},CrowdDis{j}] = EnSelection(SubPopulation{j},SubPopulationCount(j)); + [FrontNo{j},I1] = sort(FrontNo{j}); + CrowdDis{j} = CrowdDis{j}(I1); + + SubDec{j} = SubDec{j}(I1,:); + + SubMask{j} = SubMask{j}(I1,:); + + SubPopulation{j} = SubPopulation{j}(I1); + count = zeros(1,max(FrontNo{j})); + for i = 1 : max(FrontNo{j}) + for m = 1 : SubPopulationCount(j) + if FrontNo{j}(m)==i + count(i) = count(i)+1; + end + end + end + NFindex = 1; + for i = 1 : max(FrontNo{j}) + NFcount = count(i); + [CrowdDis{j}(NFindex:NFindex+NFcount-1),I2] = sort(-CrowdDis{j}(NFindex:NFindex+NFcount-1)); + tempPop = SubPopulation{j}(NFindex:NFindex+NFcount-1); + + tempDec = SubDec{j}(NFindex:NFindex+NFcount-1,:); + tempMask = SubMask{j}(NFindex:NFindex+NFcount-1,:); + + tempPop = tempPop(I2); + + tempDec = tempDec(I2,:); + tempMask = tempMask(I2,:); + + SubPopulation{j}(NFindex:NFindex+NFcount-1) = tempPop; + + SubDec{j}(NFindex:NFindex+NFcount-1,:) = tempDec; + SubMask{j}(NFindex:NFindex+NFcount-1,:) = tempMask; + + NFindex = NFindex+NFcount; + end + end + for i = 1 : size(SubPopulation,2) + Rank{i} = 1:SubPopulationCount(i); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/Operator.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/Operator.m new file mode 100644 index 000000000..63d0dc796 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/Operator.m @@ -0,0 +1,43 @@ +function [OffDec,OffMask] = Operator(Population,Dec,Mask,Fitness,winIndex,loseIndex1,loseIndex2,Problem,thetamid,REAL) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + SubPopulation = {}; + SubPopulation{1} = Population(winIndex); + SubPopulation{2} = Population(loseIndex1); + SubPopulation{3} = Population(loseIndex2); + + SubDec = {}; + SubDec{1} = Dec(winIndex,:); + SubDec{2} = Dec(loseIndex1,:); + SubDec{3} = Dec(loseIndex2,:); + + SubMask = {}; + SubMask{1} = Mask(winIndex,:); + SubMask{2} = Mask(loseIndex1,:); + SubMask{3} = Mask(loseIndex2,:); + + [SubPopulation,SubDec,SubMask,Rank] = InitRank(SubPopulation,SubDec,SubMask,Problem); + [OffDec1,OffMask1] = OperatorWin(Problem,SubPopulation{1},SubDec{1},SubMask{1},Rank{1},Fitness,REAL); + if size(SubPopulation{2},2)>0 + [OffDec2,OffMask2] = OperatorMin(Problem,SubPopulation{1},SubDec{1},SubMask{1},Rank{1},SubPopulation{2},SubDec{2},SubMask{2},Rank{2},Fitness,thetamid,REAL); + else + OffDec2 = []; + OffMask2 = []; + end + if size(SubPopulation{3},2)>0 + [OffDec3,OffMask3] = OperatorMax(Problem,SubPopulation{1},SubDec{1},SubMask{1},Rank{1},SubPopulation{3},SubDec{3},SubMask{3},Rank{3},Fitness,thetamid,REAL); + else + OffDec3 = []; + OffMask3 = []; + end + OffDec = [OffDec1;OffDec2;OffDec3]; + OffMask = [OffMask1;OffMask2;OffMask3]; +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMax.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMax.m new file mode 100644 index 000000000..a59493e04 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMax.m @@ -0,0 +1,61 @@ +function [OffDec,OffMask] = OperatorMax(Problem,winpop,windec,winmask,winrank,SubPopulation,SubDec,SubMask,Rank,Fitness,thetamid,REAL) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + winPool = TournamentSelection(2,size(SubPopulation,2),winrank); + Parent1Dec = windec(winPool,:); + Parent1Mask = winmask(winPool,:); + + MatingPool = TournamentSelection(2,size(SubPopulation,2),Rank); + Parent2Dec = SubDec(MatingPool,:); + Parent2Mask = SubMask(MatingPool,:); + + ParentDec = [Parent1Dec;Parent2Dec]; + [N,D] = size(ParentDec); + + %% Perform uniform crossover and bit-flip mutation for Mask + % Uniform crossover + k = rand(N/2,D) < 0.5; + k(repmat(rand(N/2,1)>1,1,D)) = false; + OffMask = Parent1Mask; + OffMask(k) = Parent2Mask(k); + + %% Mutation for mask + for i = 1 : N/2 + if sum(OffMask(i,:))<=thetamid + % Bit-flip mutation + Site = rand(1,D) < 1/D; + OffMask(i,Site) = ~OffMask(i,Site); + else + index1 = find(OffMask(i,:)); + index0 = find(~OffMask(i,:)); + maxN = floor(size(index1,2) - thetamid); + index = index1(TSmin(-Fitness(index1),maxN)); + OffMask(i,index) = 0; + end + end + + %% Crossover and mutation for dec + if REAL + OffDec = OperatorGAhalf(Problem,ParentDec); + else + OffDec = ones(N/2,D); + end +end + +function index = TSmin(Fitness,minN) +% Binary tournament selection + + if isempty(Fitness) + index = []; + else + index = TournamentSelection(2,minN,Fitness); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMin.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMin.m new file mode 100644 index 000000000..cb38b732f --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorMin.m @@ -0,0 +1,61 @@ +function [OffDec,OffMask] = OperatorMin(Problem,winpop,windec,winmask,winrank,SubPopulation,SubDec,SubMask,Rank,Fitness,thetamid,REAL) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + winPool = TournamentSelection(2,size(SubPopulation,2),winrank); + Parent1Dec = windec(winPool,:); + Parent1Mask = winmask(winPool,:); + + MatingPool = TournamentSelection(2,size(SubPopulation,2),Rank); + Parent2Dec = SubDec(MatingPool,:); + Parent2Mask = SubMask(MatingPool,:); + + ParentDec = [Parent1Dec;Parent2Dec]; + [N,D] = size(ParentDec); + + %% Perform uniform crossover and bit-flip mutation for Mask + % Uniform crossover + k = rand(N/2,D) < 0.5; + k(repmat(rand(N/2,1)>1,1,D)) = false; + OffMask = Parent1Mask; + OffMask(k) = Parent2Mask(k); + + %% Mutation for mask + for i = 1 : N/2 + if sum(OffMask(i,:))>=thetamid + % Bit-flip mutation + Site = rand(1,D) < 1/D; + OffMask(i,Site) = ~OffMask(i,Site); + else + index1 = find(OffMask(i,:)); + index0 = find(~OffMask(i,:)); + minN = floor(thetamid - size(index1,2)); + index = index0(TSmin(Fitness(index0),minN)); + OffMask(i,index) = 1; + end + end + + %% Crossover and mutation for dec + if REAL + OffDec = OperatorGAhalf(Problem,ParentDec); + else + OffDec = ones(N/2,D); + end +end + +function index = TSmin(Fitness,minN) +% Binary tournament selection + + if isempty(Fitness) + index = []; + else + index = TournamentSelection(2,minN,Fitness); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorWin.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorWin.m new file mode 100644 index 000000000..660aefbd8 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/OperatorWin.m @@ -0,0 +1,49 @@ +function [OffDec,OffMask] = OperatorWin(Problem,SubPopulation,SubDec,SubMask,Rank,Fitness,REAL) + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + MatingPool = TournamentSelection(2,2*size(SubPopulation,2),Rank); + ParentDec = SubDec(MatingPool,:); + ParentMask = SubMask(MatingPool,:); + %% Parameter setting + [N,D] = size(ParentDec); + randN = randperm(N); + Parent1Dec = ParentDec(randN(1:N/2),:); + Parent2Dec = ParentDec(randN(N/2+1:end),:); + Parent1Mask = ParentMask(randN(1:N/2),:); + Parent2Mask = ParentMask(randN(N/2+1:end),:); + + %% Perform uniform crossover and bit-flip mutation for Mask + % Uniform crossover + k = rand(N/2,D) < 0.5; + k(repmat(rand(N/2,1)>1,1,D)) = false; + OffMask = Parent1Mask; + OffMask(k) = Parent2Mask(k); + % Bit-flip mutation + Site = rand(N/2,D) < 1/D; + OffMask(Site) = ~OffMask(Site); + + %% Crossover and mutation for dec + if REAL + OffDec = OperatorGAhalf(Problem,ParentDec); + else + OffDec = ones(N/2,D); + end +end + +function index = TS(Fitness) +% Binary tournament selection + + if isempty(Fitness) + index = []; + else + index = TournamentSelection(2,1,Fitness); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/SGECF.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/SGECF.m new file mode 100644 index 000000000..131701b19 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/SGECF.m @@ -0,0 +1,112 @@ +classdef SGECF < ALGORITHM +% +% Sparsity-guided elitism co-evolutionary framework + +%------------------------------- Reference -------------------------------- +% C. Wu, Y. Tian, Y. Zhang, H. Jiang, and X. Zhang, A sparsity-guided +% elitism co-evolutionary framework for sparse large-scale multi-objective +% optimization, Proceedings of the IEEE Congress on Evolutionary +% Computation, 2023. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + methods + function main(Algorithm,Problem) + %% Population initialization + % Calculate the fitness of each decision variable + TDec = []; + TMask = []; + TempPop = []; + Fitness = zeros(1,Problem.D); + REAL = ~strcmp(Problem.encoding,'binary'); + for i = 1 : 1+3*REAL + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.D,1),repmat(Problem.upper,Problem.D,1)); + else + Dec = ones(Problem.D,Problem.D); + end + Mask = eye(Problem.D); + Population = Problem.Evaluation(Dec.*Mask); + TDec = [TDec;Dec]; + TMask = [TMask;Mask]; + TempPop = [TempPop,Population]; + Fitness = Fitness + NDSort([Population.objs,Population.cons],inf); + end + % Calculate the theta + if REAL + VDec = unifrnd(repmat(Problem.lower,Problem.D,1),repmat(Problem.upper,Problem.D,1)); + else + VDec = ones(Problem.D,Problem.D); + end + VMask = zeros(Problem.D,Problem.D); + for i = 1 : Problem.D + [~,rank1] = sort(Fitness); + VMask(i,rank1(1:i)) = 1; + end + VPopulation = Problem.Evaluation(VDec.*VMask); + [VPopulation,VDec,VMask,FrontNo,CrowdDis] = SPEA2_EnvironmentalSelection([VPopulation,TempPop],[VDec;TDec],[VMask;TMask],Problem.N); + pop = VPopulation(find(FrontNo ==1)); + popMask = VMask(find(FrontNo ==1),:); + thetaAll = sum(popMask,2)'; + thetaUnique = unique(thetaAll); + if mod(size(thetaUnique,2),2)==0 + minOdd = max(1,size(thetaAll,2)-1); + else + minOdd = max(1,size(thetaAll,2)-2); + end + [~,theta] = kmeans(thetaAll',minOdd); + thetamid = median(theta); + + % Generate initial population + Dec = []; + Mask = []; + % Generate initial population + if REAL + Dec = unifrnd(repmat(Problem.lower,Problem.N,1),repmat(Problem.upper,Problem.N,1)); + else + Dec = ones(Problem.N,Problem.D); + end + Mask = zeros(Problem.N,Problem.D); + for i = 1 : Problem.N + Mask(i,TournamentSelection(2,ceil(rand*Problem.D),Fitness)) = 1; + end + Population = Problem.Evaluation(Dec.*Mask); + [Population,Dec,Mask,FrontNo,CrowdDis] = SPEA2_EnvironmentalSelection([VPopulation,Population],[VDec;Dec],[VMask;Mask],Problem.N); + + temptheta = sum(Mask,2)'; + + %% Optimization + while Algorithm.NotTerminated(Population) + winIndex = find(FrontNo ==1); + winPopMask = Mask(find(FrontNo ==1),:); + winTheta = sum(winPopMask,2)'; + thetaUnique = unique(winTheta); + if mod(size(thetaUnique,2),2)==0 + minOdd = max(1,size(winTheta,2)-1); + else + minOdd = max(1,size(winTheta,2)-2); + end + [~,theta] = kmeans(winTheta',minOdd); + thetamid = median(theta); + + loseIndex = find(FrontNo ~=1); + losePopMask = Mask(find(FrontNo ~=1),:); + loseTheta = sum(losePopMask,2)'; + + loseIndex1 = loseIndex(find(loseTheta<=thetamid)); + loseIndex2 = loseIndex(find(loseTheta>thetamid)); + + [OffDec,OffMask] = Operator(Population,Dec,Mask,Fitness,winIndex,loseIndex1,loseIndex2,Problem,thetamid,REAL); + Offspring = Problem.Evaluation(OffDec.*OffMask); + [Population,Dec,Mask,FrontNo,CrowdDis] = SPEA2_EnvironmentalSelection([Population,Offspring],[Dec;OffDec],[Mask;OffMask],Problem.N); + Theta = sum(Mask,2)'; + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/SGECF/SPEA2_EnvironmentalSelection.m b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/SPEA2_EnvironmentalSelection.m new file mode 100644 index 000000000..410698244 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/SGECF/SPEA2_EnvironmentalSelection.m @@ -0,0 +1,55 @@ +function [Population,Dec,Mask,FrontNo,CrowdDis] = SPEA2_EnvironmentalSelection(Population,Dec,Mask,N) +% Environmental selection + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + + %% Delete duplicated solutions + [~,uni] = unique(Population.objs,'rows'); + Population = Population(uni); + Dec = Dec(uni,:); + Mask = Mask(uni,:); + N = min(N,length(Population)); + %% Calculate the fitness of each solution + + [FrontNo,MaxFNo] = NDSort(Population.objs,N); + Next = false(1,length(FrontNo)); + Next(FrontNo=N + break; + end + end + + Level=LevelSort(ndsPopulation,n); + levelPopulation=[]; + for i=1:n + levelPopulation=cat(2,levelPopulation,ndsPopulation(Level==i)); + %levelPopulation=[levelPopulation;ndsPopulation((Level==i),:)]; + if size(levelPopulation,1)>=N + break; + end + end + + ndsPopulation=levelPopulation; + + + Population=[]; + for i=1:size(W,1) + Zmax=max(ndsPopulation.objs,[],1); + Zmin=min(ndsPopulation.objs,[],1); + SPopObj=(ndsPopulation.objs-repmat(Zmin,size(ndsPopulation.objs,1),1))./(repmat(Zmax,size(ndsPopulation.objs,1),1)-repmat(Zmin,size(ndsPopulation.objs,1),1)); + [~,index] = max(1-pdist2(SPopObj,W(i,:),'cosine')); + Population=[Population,ndsPopulation(index)]; + ndsPopulation=setdiff(ndsPopulation,ndsPopulation(index)); + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/LevelSort.m b/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/LevelSort.m new file mode 100644 index 000000000..df156d76b --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/LevelSort.m @@ -0,0 +1,36 @@ +function Level = LevelSort(Population,n) +% Ssort the level of Population + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + Zmax = max(Population.objs,[],1); + Zmin = min(Population.objs,[],1); + interval = (Zmax-Zmin)./n; + Level = zeros(length(Population),1); + objs = Population.objs; + for i = 1 : length(Population) + t = 0; + leveled = 0; + obj = objs(i,:); + while leveled == 0 + t = t+1; + leveled = 1; + for j = 1 : size(objs,2) + if obj(1,j)>Zmin(j)+(t+1)*interval(1,j) + leveled = 0; + break; + end + end + end + Level(i) = t; + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/SPDSort.m b/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/SPDSort.m new file mode 100644 index 000000000..46d720050 --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/SPDSort.m @@ -0,0 +1,65 @@ +function [FrontNo,MaxFNo] = SPDSort(PopObj,d1,d2,RP,nSort) +% SPD sorting + +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + [N,M] = size(PopObj); + FrontNo = inf(1,N); + MaxFNo = 0; + while sum(FrontNo PopObj(j,m) + if domi == 1 + domi = 0; + break; + else + domi = -1; + end + end + end + if domi == 0 && RP(i)==RP(j) + if d1(i)+5*d2(i) < d1(j)+5*d2(j) + domi = 1; + elseif d1(i)+5*d2(i) > d1(j)+5*d2(j) + domi = -1; + end + end + if domi == 1 + D(j) = true; + elseif domi == -1 + D(i) = true; + break; + end + end + end + if ~D(i) + FrontNo(i) = MaxFNo; + end + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/TSNSGAII.m b/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/TSNSGAII.m new file mode 100644 index 000000000..0208fa4ed --- /dev/null +++ b/PlatEMO/Algorithms/Multi-objective optimization/TS-NSGA-II/TSNSGAII.m @@ -0,0 +1,44 @@ +classdef TSNSGAII < ALGORITHM +% +% Two stage NSGA-II + +%------------------------------- Reference -------------------------------- +% F. Ming, W. Gong, and L. Wang, A two-stage evolutionary algorithm with +% balanced convergence and diversity for many-objective optimization, IEEE +% Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(10): +% 6222-6234. +%------------------------------- Copyright -------------------------------- +% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for +% research purposes. All publications which use this platform or any code +% in the platform should acknowledge the use of "PlatEMO" and reference "Ye +% Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform +% for evolutionary multi-objective optimization [educational forum], IEEE +% Computational Intelligence Magazine, 2017, 12(4): 73-87". +%-------------------------------------------------------------------------- + +% This function is written by Fei Ming + + methods + function main(Algorithm,Problem) + %% Generate the weight vectors + [W,Problem.N] = UniformPoint(Problem.N,Problem.M); + + %% Generate random population + Population = Problem.Initialization(); + [~,FrontNo,d2] = EnvironmentalSelection(Population,W,Problem.N); + + %% Optimization + while Algorithm.NotTerminated(Population) + MatingPool = TournamentSelection(2,Problem.N,FrontNo,d2); + Offspring = OperatorGA(Problem,Population(MatingPool)); + if Problem.FE<(0.8+0.2*(1-3/Problem.M))*Problem.maxFE + [Population,FrontNo,d2] = EnvironmentalSelection([Population,Offspring],W,Problem.N); + else + Population = EnvironmentalSelection1([Population,Offspring],W,Problem.N,Problem.M); + FrontNo = NDSort(Population.objs,inf); + d2 = DensityEstimate(Population,W); + end + end + end + end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Multi-objective optimization/URCMO/URCMO.m b/PlatEMO/Algorithms/Multi-objective optimization/URCMO/URCMO.m index fcd00c5a4..7f51371a7 100644 --- a/PlatEMO/Algorithms/Multi-objective optimization/URCMO/URCMO.m +++ b/PlatEMO/Algorithms/Multi-objective optimization/URCMO/URCMO.m @@ -52,7 +52,7 @@ function main(Algorithm,Problem) succ_jilu(cnt,3) = sum(succ2(1:Problem.N/2)); succ_jilu(cnt,4) = sum(succ2(1 + Problem.N/2 : Problem.N)); % only succ2 is used in the paper - if Problem.FE == first_FES + if Problem.FE >= first_FES for num = 1 : 4 if std(succ_jilu(:,num)) ~= 0 a(num) = mean(succ_jilu(:,num)) / std(succ_jilu(:,num)); diff --git a/PlatEMO/Algorithms/Single-objective optimization/GA/GA.m b/PlatEMO/Algorithms/Single-objective optimization/GA/GA.m index 00266ce83..5031dbbd8 100644 --- a/PlatEMO/Algorithms/Single-objective optimization/GA/GA.m +++ b/PlatEMO/Algorithms/Single-objective optimization/GA/GA.m @@ -36,4 +36,4 @@ function main(Algorithm,Problem) end end end -end +end \ No newline at end of file diff --git a/PlatEMO/Algorithms/Utility functions/OperatorPSO.m b/PlatEMO/Algorithms/Utility functions/OperatorPSO.m index c992bac39..e72cad78f 100644 --- a/PlatEMO/Algorithms/Utility functions/OperatorPSO.m +++ b/PlatEMO/Algorithms/Utility functions/OperatorPSO.m @@ -42,5 +42,5 @@ r2 = rand(N,D); OffVel = W.*ParticleVel + r1.*(PbestDec-ParticleDec) + r2.*(GbestDec-ParticleDec); OffDec = ParticleDec + OffVel; - Offspring = Problem.Evaluation(OffDec); + Offspring = Problem.Evaluation(OffDec,OffVel); end \ No newline at end of file diff --git a/PlatEMO/GUI/GUI.m b/PlatEMO/GUI/GUI.m index ebc4f55d3..74d36da8a 100644 --- a/PlatEMO/GUI/GUI.m +++ b/PlatEMO/GUI/GUI.m @@ -25,7 +25,7 @@ obj.readList(); % Create the window - obj.app.figure = uifigure('Name','PlatEMO v4.1','Position',[0 0 1200 650],'Interruptible','off','icon',obj.icon.logo1,'BusyAction','cancel','Visible','off','WindowButtonMotionFcn',@(~,~)[]); + obj.app.figure = uifigure('Name','PlatEMO v4.2','Position',[0 0 1200 650],'Interruptible','off','icon',obj.icon.logo1,'BusyAction','cancel','Visible','off','WindowButtonMotionFcn',@(~,~)[]); obj.app.maingrid = uigridlayout(obj.app.figure,'RowHeight',{25,80,'1x'},'ColumnWidth',{'1x'},'Padding',[0 0 0 0],'RowSpacing',0); % Create the tab buttons @@ -37,7 +37,7 @@ tempPanel = GUI.APP(1,4,uipanel(obj.app.grid(1),'BorderType','none','BackgroundColor',[0 .25 .45])); tempImage = uiimage(tempPanel,'Position',[0 3 99 21],'ImageSource',obj.icon.bar); tempPanel2 = uipanel(tempPanel,'Position',[13 4 18 18],'BorderType','none','BackgroundColor',[.549 .6627 .7529]); - obj.app.buttonT(3) = uibutton(tempPanel2,'Position',[-2 -2 24 24],'Text','','Icon',obj.icon.researchgate,'BackgroundColor',[.549 .6627 .7529],'Tooltip','ResearchGate','ButtonpushedFcn',@(~,~)web('https://www.researchgate.net/publication/312061859','-browser')); + obj.app.buttonT(3) = uibutton(tempPanel2,'Position',[-2 -2 24 24],'Text','','Icon',obj.icon.researchgate,'BackgroundColor',[.549 .6627 .7529],'Tooltip','ResearchGate','ButtonpushedFcn',@(~,~)web('https://www.researchgate.net/publication/365027806','-browser')); tempPanel2 = uipanel(tempPanel,'Position',[43 4 18 18],'BorderType','none','BackgroundColor',[.549 .6627 .7529]); obj.app.buttonT(4) = uibutton(tempPanel2,'Position',[-2 -2 24 24],'Text','','Icon',obj.icon.github,'BackgroundColor',[.549 .6627 .7529],'Tooltip','GitHub','ButtonpushedFcn',@(~,~)web('https://github.com/BIMK/PlatEMO','-browser')); tempPanel2 = uipanel(tempPanel,'Position',[73 4 18 18],'BorderType','none','BackgroundColor',[.549 .6627 .7529]); diff --git a/PlatEMO/GUI/module_app.m b/PlatEMO/GUI/module_app.m index 555115ed5..c01cab3a3 100644 --- a/PlatEMO/GUI/module_app.m +++ b/PlatEMO/GUI/module_app.m @@ -68,7 +68,7 @@ obj.app.buttonB(4) = GUI.APP(1,2,uibutton(tempGrid,'Text','Add','FontSize',10,'Tooltip','Add an initialization function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,2})); obj.app.buttonB(5) = GUI.APP(1,3,uibutton(tempGrid,'Text','Del','Enable',false,'FontSize',10,'Tooltip','Delete the initialization function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_updateProblem,-2})); obj.app.labelB(7) = GUI.APP(10,1,uilabel(obj.app.grid(2),'Text','I(N) =','HorizontalAlignment','right')); - obj.app.editB(5) = GUI.APP(10,[2 4],uieditfield(obj.app.grid(2),'Value','rand(N,10)','Tooltip','Function for generating an initial population with size n')); + obj.app.editB(5) = GUI.APP(10,[2 4],uieditfield(obj.app.grid(2),'Value','rand(N,10)','Tooltip','Function for generating an initial population with size N')); obj.app.buttonB(6) = GUI.APP(10,5,uibutton(obj.app.grid(2),'Text','...','Tooltip','Load existing function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_loadFunction,obj.app.editB(5),{'*.m','MATLAB function'}})); obj.app.buttonB(7) = GUI.APP(10,6,uibutton(obj.app.grid(2),'Text','+','Fontsize',15,'Tooltip','Create new function','BackgroundColor','w','ButtonpushedFcn',{@obj.cb_createFunction,obj.app.editB(5),'initialization'})); % Repair function diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP1.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP1.m index a108c70ec..aaa234b50 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP1.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP1.m @@ -1,6 +1,6 @@ classdef C10MOP1 < EvoXBenchProblem % -% EvoXBench +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP2.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP2.m index 3a56c84f2..9ba7e1a0c 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP2.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP2.m @@ -1,5 +1,6 @@ classdef C10MOP2 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP3.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP3.m index 1f388f570..3ef704096 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP3.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP3.m @@ -1,5 +1,6 @@ classdef C10MOP3 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP4.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP4.m index 14373fcda..7953759d9 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP4.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP4.m @@ -1,5 +1,6 @@ classdef C10MOP4 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP5.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP5.m index 1845bb502..9efab9aa2 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP5.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP5.m @@ -1,5 +1,6 @@ classdef C10MOP5 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP6.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP6.m index e3f89ddcd..36df99b46 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP6.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP6.m @@ -1,5 +1,6 @@ classdef C10MOP6 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP7.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP7.m index 0d9537199..0651cecd9 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP7.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP7.m @@ -1,5 +1,6 @@ classdef C10MOP7 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP8.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP8.m index 4de18b666..91772b5e0 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP8.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP8.m @@ -1,5 +1,6 @@ classdef C10MOP8 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP9.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP9.m index 707e635f5..a9af5575f 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP9.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/C10MOP9.m @@ -1,5 +1,6 @@ classdef C10MOP9 < EvoXBenchProblem % +% EvoXBench on CIFAR-10 %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP1.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP1.m index d9ae9acc9..93558f099 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP1.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP1.m @@ -1,5 +1,6 @@ classdef IN1KMOP1 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP2.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP2.m index 51e7cc546..504bddd72 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP2.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP2.m @@ -1,5 +1,6 @@ classdef IN1KMOP2 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP3.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP3.m index 41668afd6..de16bafa4 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP3.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP3.m @@ -1,5 +1,6 @@ classdef IN1KMOP3 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP4.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP4.m index 44ce2ebee..1e6d3ef55 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP4.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP4.m @@ -1,5 +1,6 @@ classdef IN1KMOP4 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP5.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP5.m index ac9fc0636..0851643bf 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP5.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP5.m @@ -1,5 +1,6 @@ classdef IN1KMOP5 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP6.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP6.m index a019ee36e..34ea13ea5 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP6.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP6.m @@ -1,5 +1,6 @@ classdef IN1KMOP6 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP7.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP7.m index 204edea99..7b2cf3243 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP7.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP7.m @@ -1,5 +1,6 @@ classdef IN1KMOP7 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP8.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP8.m index 2a319223b..3f3990884 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP8.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP8.m @@ -1,5 +1,6 @@ classdef IN1KMOP8 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP9.m b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP9.m index 60f8118d1..5576a5d9f 100644 --- a/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP9.m +++ b/PlatEMO/Problems/Multi-objective optimization/EvoXBench/IN1KMOP9.m @@ -1,5 +1,6 @@ classdef IN1KMOP9 < EvoXBenchProblem % +% EvoXBench on ImageNet 1K %------------------------------- Reference -------------------------------- % Z. Lu, R. Cheng, Y. Jin, K. C. Tan, and K. Deb, Neural architecture diff --git a/PlatEMO/Problems/Multi-objective optimization/RWMOPs/RWMOP50.m b/PlatEMO/Problems/Multi-objective optimization/RWMOPs/RWMOP50.m index eaec2066c..7bd3bdbee 100644 --- a/PlatEMO/Problems/Multi-objective optimization/RWMOPs/RWMOP50.m +++ b/PlatEMO/Problems/Multi-objective optimization/RWMOPs/RWMOP50.m @@ -44,7 +44,7 @@ function Setting(obj) PL = zeros(size(x,1),1); for i = 1 : size(x,2) for j = 1 : size(x,2) - PL = PL + x(:,i) .* B(i,j) .* x(j); + PL = PL + x(:,i) .* B(i,j) .* x(:,j); end end % Objectives diff --git a/PlatEMO/manual (Chinese) .pdf b/PlatEMO/manual (Chinese) .pdf index e3290e6be18e27e3d7869c31bd1d77a0a08b9015..7f35569d84a8bdc1d0782dc23ff169d520cbda03 100644 GIT binary patch delta 771357 zcmZ5`V|1WFvu14D=EP1WHYZLdwrwYGl8J5GwmGqF+r}gl?|k>}p1r$&x>~2ZtDdS; z)zzKxTRZGQ7oAd3OoD-hkqeP>|6pbT5srxlsfxpD;D*90`g-%M zDA}BY$;60Nu3U;d&9b-^?sJ{oq-klI#8JgvX3*TA6d$A2UNmZ{oP#0mH2&*Xv(h%L zn;zW_=7|4G8A5THkMgi(HQ3`&Rb&z+Sg;%XoSitlvP+nMu$y`k)LsU zZ;{-(T!xc`>>(Yccorz5XnV^X4@WePrb<=C6_DJQTNN>Fuq_;vErUWj`~@4kYM>It zljuJ3*JAjyX=voszn2jZF?;a*q7oe98$*|h?~9Q#&gmLcU>AV(_m@OPZy83=Y~eJ3 zfJHLFzz1#}`|WUX5)I+s>*QFGoDfLzaMB#~(wA0Yj8#k!w+FG%mO)KuhG?{;G^E

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