基于多目标粒子群算法的支配解求解，基于多目标粒子群的帕累托前沿求解，基于mopso的多目标求解，基于多目标粒子群的模糊优化算法

clc; clear; close all; %导入数据 load maydata.mat %% Problem Definition CostFunction=@(x,x1) fun(x,x1); % 适应度函数 nVar=24; % Number of Decision Variables VarSize=[1 nVar]; % Size of Decision Variables Matrix ct = [0.4 0.4 0.4 0.4 0.4 0.4 0.4 2 2 2 2 1.2 1.2 1.2 2 2 2 2 2 1.2 1.2 1.2 1.2 1.2]; ita = (max(ct)-min(ct))/max(ct); VarMin= -3000*ones(1,24); VarMax = 3000*ones(1,24); % VarMin=[56 8 23 800 6 4 14];%下限r1 h1 h2 N r2 r3 r4 % VarMax=[65 12 27 3200 12 8 18]; % 上限 %% MOPSO Parameters MaxIt=300; %最大迭代次数 nPop=200; % 种群数目 nRep=50; % 帕累托解集数目 w=0.5; % 惯性权重 wdamp=0.99; % 权重变化率 c1=2; %个体学习因子 c2=2; % 群体学习因子 nGrid=7; % 每维网格数目 alpha=0.2; % 边界扩展系数 beta=2; % Leader Selection Pressure gamma=2; % Deletion Selection Pressure mu=0.1; % Mutation Rate %% 初始化 empty_particle.Position=[]; empty_particle.Velocity=[]; empty_particle.Cost=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; empty_particle.IsDominated=[]; empty_particle.GridIndex=[]; empty_particle.GridSubIndex=[]; pop=repmat(empty_particle,nPop,1); p1 = []; for ii=1:nPop pop(ii).Position=unifrnd(VarMin,VarMax,VarSize);%粒子位置 pop(ii).Velocity=zeros(VarSize);%粒子速度 % pop(ii).Cost=CostFunction(pop(ii).Position);%适应度值 p1 = [p1;pop(ii).Position]; pop(ii).Best.Position=pop(ii).Position; end pcost1 = CostFunction(p1,x1);%适应度值 for ii = 1:nPop pop(ii).Cost=pcost1(:,ii);%适应度值 pop(ii).Best.Cost=pop(ii).Cost; end % 支配解集 pop=DetermineDomination(pop); rep=pop(~[pop.IsDominated]); Grid=CreateGrid(rep,nGrid,alpha); for ii=1:numel(rep) rep(ii)=FindGridIndex(rep(ii),Grid); end %% 主循环 for it=1:MaxIt p2 = []; p3 = []; p4 = []; p5 = []; p6 = []; pcost2 = []; for ii=1:nPop leader=SelectLeader(rep,beta);% pop(ii).Velocity = w*pop(ii).Velocity ... +c1*rand(VarSize).*(pop(ii).Best.Position-pop(ii).Position) ... +c2*rand(VarSize).*(leader.Position-pop(ii).Position); pop(ii).Position = pop(ii).Position + pop(ii).Velocity; pop(ii).Position = max(pop(ii).Position, VarMin); pop(ii).Position = min(pop(ii).Position, VarMax); % pop(ii).Cost = CostFunction(pop(ii).Position); p2 = [p2;pop(ii).Position]; % Apply Mutation pm=(1-(it-1)/(MaxIt-1))^(1/mu); if rand<pm NewSol.Position=Mutate(pop(ii).Position,pm,VarMin,VarMax); p3 = [p3; NewSol.Position]; p4 = [p4;ii]; NewSol.Cost=CostFunction(NewSol.Position,x1); if Dominates(NewSol,pop(ii)) pop(ii).Position=NewSol.Position; pop(ii).Cost=NewSol.Cost; elseif Dominates(pop(ii),NewSol) % Do Nothing else if rand<0.5 pop(ii).Position=NewSol.Position; pop(ii).Cost=NewSol.Cost; end end end end p5 = [p2;p3]; pcost2 = CostFunction(p5,x1);%适应度值 for ii = 1:nPop pop(ii).Cost=pcost2(:,ii);%适应度值 end p6 = pcost2(:,nPop+1:end); % isempty(p6) pan = 1; for ii = 1:nPop if ~isempty(p6) && ii == p6(pan) NewSol.Position=Mutate(pop(ii).Position,pm,VarMin,VarMax); NewSol.Cost=p6(:,pan); pan = pan + 1; if Dominates(pop(ii),pop(ii).Best) pop(ii).Best.Position=pop(ii).Position; pop(ii).Best.Cost=pop(ii).Cost; elseif Dominates(pop(ii).Best,pop(ii)) % Do Nothing else if rand<0.5 pop(ii).Best.Position=pop(ii).Position; pop(ii).Best.Cost=pop(ii).Cost; end end end end % 将非支配粒子添加到储存库中 rep=[rep;pop(~[pop.IsDominated])]; %#ok % 确定新的非劣解解集 rep=DetermineDomination(rep); % Keep only Non-Dminated Memebrs in the Repository rep=rep(~[rep.IsDominated]); % Update Grid Grid=CreateGrid(rep,nGrid,alpha); % Update Grid Indices for ii=1:numel(rep) rep(ii)=FindGridIndex(rep(ii),Grid); end % Check if Repository is Full if numel(rep)>nRep Extra=numel(rep)-nRep; for e=1:Extra rep=DeleteOneRepMemebr(rep,gamma); end end % if it==MaxIt figure(1); PlotCosts(pop,rep); pause(0.01); % end % % Show Iteration Information disp(['Iteration ' num2str(it) ': Number of Rep Members = ' num2str(numel(rep))]); % Damping Inertia Weight w=w*wdamp; % if it == MaxIt % % hold on % plot3(T_test(1,:),T_test(2,:),T_test(3,:),'ko') % end end