platemo4.3

PlatEMO/Algorithms/Multi-objective optimization/CMEGL/CMEGL.m

@@ -0,0 +1,96 @@
+classdef CMEGL < ALGORITHM
+% <multi> <real/integer/label/binary/permutation> <constrained>
+% Constrained evolutionary multitasking with global and local auxiliary tasks
+
+%------------------------------- Reference --------------------------------
+% K. Qiao, J. Liang, Z. Liu, K. Yu, C. Yue, and B. Qu, Evolutionary
+% multitasking with global and local auxiliary tasks for constrained
+% multi-objective optimization, IEEE/CAA Journal of Automatica Sinica,
+% 2023, 10(10): 1951-1964.
+%------------------------------- 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 MaOperatorGAzine, 2017, 12(4): 73-87".
+%--------------------------------------------------------------------------
+
+% This function is written by Kangjia Qiao (email: [email protected])
+
+    methods
+        function main(Algorithm,Problem)
+            %% Generate random population
+            Population1 = Problem.Initialization(); % Main task
+            Fitness1    = CalFitness(Population1.objs,Population1.cons);
+            Population2 = Problem.Initialization(); % Global auxiliary task
+            Fitness2   = CalFitness(Population2.objs);
+            Population3 = Problem.Initialization(); % Local auxiliary task
+            Fitness3   = CalFitness(Population3.objs,Population3.cons);
+
+            % Calculate the constraint boundary of local auxiliary task
+            cons = Population1.cons;
+            cons(cons<0) = 0;
+            cons =sum(cons,2);
+            index =find(cons>0);
+            if isempty(index)
+                VAR0 = 0;
+            else
+                VAR0 =  mean(cons(index));
+            end
+            cnt  = 0;	% index of generation
+            flag = 0;
+
+            %% Optimization
+            while Algorithm.NotTerminated(Population1)
+                cnt =cnt +1;
+                if flag == 0
+                    std_obj(cnt,:) = std(Population2.objs,[],1);
+                    if cnt>100
+                        if  sum(std(std_obj(cnt-100:cnt,:),[],1)<0.5) == Problem.M
+                            flag = 1;
+                        end
+                    end
+                end
+                %% Offspring generation
+                MatingPool = TournamentSelection(2,Problem.N,Fitness1);
+                Offspring1 = OperatorGAhalf(Problem,[Population1(MatingPool)]);
+
+                if flag == 0
+                    MatingPool = TournamentSelection(2,Problem.N,Fitness2);
+                    Offspring2 = OperatorGAhalf(Problem,[Population2(MatingPool)]);
+                else
+                    Offspring2 = [];
+                end
+
+                if length(Population3) <=1
+                    Offspring3 = [];
+                else
+                    MatingPool = TournamentSelection(2,min(length(Population3),Problem.N/2),Fitness3);
+                    Offspring3 = OperatorGA(Problem,[Population3(MatingPool)]);
+                end
+
+                %% Environmental selection
+                [Population1,Fitness1] = EnvironmentalSelection([Population1,Offspring2,Offspring3],Problem.N,true);  
+                [Population1,Fitness1] = EnvironmentalSelection([Population1,Offspring1],Problem.N,true);  
+
+                if flag == 0
+                    [Population2,Fitness2] = EnvironmentalSelection([Population2,Offspring1,Offspring2,Offspring3],Problem.N,false);
+                end
+
+                [Population3,Fitness3] = EnvironmentalSelection_LAT([Population3,Offspring1,Offspring2,Offspring3],Problem.N,VAR0);
+
+                % Calculate the constraint boundary of local auxiliary task
+                cons = Offspring1.cons;
+                cons(cons<0) = 0;
+                cons = sum(cons,2);
+                index = find(cons>0);
+                if isempty(index)
+                    VAR0 = 0;
+                else
+                    VAR0 = mean(cons(index));
+                end
+            end
+        end
+    end
+end

PlatEMO/Algorithms/Multi-objective optimization/CMEGL/CalFitness.m

@@ -0,0 +1,56 @@
+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".
+%--------------------------------------------------------------------------
+
+    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,:)) - 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

PlatEMO/Algorithms/Multi-objective optimization/CMEGL/EnvironmentalSelection.m

@@ -0,0 +1,51 @@
+function [Population,Fitness] = EnvironmentalSelection(Population,N,isOrigin)
+% 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
+    if isOrigin 
+        Fitness = CalFitness(Population.objs,Population.cons);
+    else
+        Fitness = CalFitness(Population.objs);
+    end
+
+    %% 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

PlatEMO/Algorithms/Multi-objective optimization/CMEGL/EnvironmentalSelection_LAT.m

@@ -0,0 +1,78 @@
+function [return_pop,return_Fitness] = EnvironmentalSelection_LAT(Population,N,VAR)
+% Multi-objective-based CHT is used to sort the Population of local auxiliary task
+
+%------------------------------- 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 Kangjia Qiao (email: [email protected])
+
+    input_cons = Population.cons;
+    input_cons(input_cons<0) = 0;
+    input_cons = sum(input_cons,2);
+
+    findex = find(input_cons<=VAR);
+    fPopulation = Population(findex);
+
+    if isempty(fPopulation)
+        fPopulation = [];
+        fFitness = [];
+    elseif length(fPopulation) <= N
+        cons = fPopulation.cons;
+        cons(cons<0)=0;
+        cons = sum(cons,2);
+        fFitness = CalFitness([fPopulation.objs,cons]);
+
+        % Sort the population
+        [fFitness,rank] = sort(fFitness);
+        fPopulation = fPopulation(rank);
+        fFitness = fFitness(rank);
+    elseif length(fPopulation) > N
+        cons = fPopulation.cons;
+        cons(cons<0)=0;
+        cons = sum(cons,2);
+        fFitness = CalFitness([fPopulation.objs,cons]);
+        Next = fFitness < 1;
+        if sum(Next) <= N
+            [~,Rank] = sort(fFitness);
+            Next(Rank(1:N )) = true;
+        elseif sum(Next) > N
+            Del  = Truncation(fPopulation(Next).objs, sum(Next)-N );
+            Temp = find(Next);
+            Next(Temp(Del)) = false;
+        end
+
+        fPopulation = fPopulation(Next);
+        fFitness   = fFitness(Next);
+        % Sort the population
+        [fFitness,rank] = sort(fFitness);
+        fPopulation = fPopulation(rank);
+
+    end
+
+    return_pop = [fPopulation];
+    return_Fitness = [fFitness];
+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);
+        if isempty(Remain)
+            keyboard
+        end
+        Temp     = sort(Distance(Remain,Remain),2);
+        [~,Rank] = sortrows(Temp);
+        Del(Remain(Rank(1))) = true;
+    end
+end

PlatEMO/Algorithms/Multi-objective optimization/CMME/CMME.m

@@ -3,7 +3,7 @@
 % 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
+% 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 --------------------------------

PlatEMO/Algorithms/Multi-objective optimization/CoMMEA/CoMMEA.m

@@ -1,6 +1,6 @@
 classdef CoMMEA < ALGORITHM
 % <multi> <real/integer/label/binary/permutation> <multimodal>
-% Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization
+% Coevolutionary multimodal multi-objective evolutionary algorithm
 % eps --- 0.2 --- Parameter for quality of the local Pareto front (suggested to be 0 if the problem has no local Pareto front).
 
 %------------------------------- Reference --------------------------------