MOEA-D算法 matlab代码.zip

clc; clear; close all; %% Problem Definition CostFunction=@(x) MOP2(x); % Cost Function (funcion obj es lo mismo) nVar=3; % Number of Decision Variables VarSize=[nVar 1]; % Decision Variables Matrix Size VarMin = 0; % Decision Variables Lower Bound VarMax = 1; % Decision Variables Upper Bound nObj=numel(CostFunction(unifrnd(VarMin,VarMax,VarSize))); %% MOEA/D Settings MaxIt=100; % Maximum Number of Iterations nPop=50; % Population Size (Number of Sub-Problems) nArchive=50; T=max(ceil(0.15*nPop),2); % Number of Neighbors T=min(max(T,2),15); crossover_params.gamma=0.5; crossover_params.VarMin=VarMin; crossover_params.VarMax=VarMax; %% Initialization % Create Sub-problems sp=CreateSubProblems(nObj,nPop,T); % Empty Individual (vacio-referencia) empty_individual.Position=[]; empty_individual.Cost=[]; empty_individual.g=[]; empty_individual.IsDominated=[]; % Initialize Goal Point %z=inf(nObj,1); z=zeros(nObj,1); % Create Initial Population(inicializar con aleatorios) pop=repmat(empty_individual,nPop,1); for i=1:nPop pop(i).Position=unifrnd(VarMin,VarMax,VarSize); %aleat.distri. pop(i).Cost=CostFunction(pop(i).Position); z=min(z,pop(i).Cost); end for i=1:nPop pop(i).g=DecomposedCost(pop(i),z,sp(i).lambda); end % Determine Population Domination Status pop=DetermineDomination(pop); % Initialize Estimated Pareto Front EP=pop(~[pop.IsDominated]); %% Main Loop for it=1:MaxIt for i=1:nPop % Reproduction (Crossover) K=randsample(T,2); j1=sp(i).Neighbors(K(1)); p1=pop(j1); j2=sp(i).Neighbors(K(2)); p2=pop(j2); y=empty_individual; y.Position=Crossover(p1.Position,p2.Position,crossover_params); y.Cost=CostFunction(y.Position); z=min(z,y.Cost); for j=sp(i).Neighbors y.g=DecomposedCost(y,z,sp(j).lambda); if y.g<=pop(j).g pop(j)=y; end end end % Determine Population Domination Status pop=DetermineDomination(pop); ndpop=pop(~[pop.IsDominated]); EP=[EP ndpop]; % ok EP=DetermineDomination(EP); EP=EP(~[EP.IsDominated]); if numel(EP)>nArchive Extra=numel(EP)-nArchive; ToBeDeleted=randsample(numel(EP),Extra); EP(ToBeDeleted)=[]; end % Plot EP figure(1); PlotCosts(EP); pause(0.01); % Display Iteration Information disp(['Iteration ' num2str(it) ': Number of Pareto Solutions = ' num2str(numel(EP))]); end %% Reults disp(' '); EPC=[EP.Cost]; for j=1:nObj disp(['Objective #' num2str(j) ':']); disp([' Min = ' num2str(min(EPC(j,:)))]); disp([' Max = ' num2str(max(EPC(j,:)))]); disp([' Range = ' num2str(max(EPC(j,:))-min(EPC(j,:)))]); disp([' St.D. = ' num2str(std(EPC(j,:)))]); disp([' Mean = ' num2str(mean(EPC(j,:)))]); disp(' '); end