基于粒子群算法PSO、帝国殖民算法ICA 和萤火虫算法 FA 求解最小生成树附matlab代码.zip

clc; clear; close all; %% Problem Definition model=CreateModel(); CostFunction=@(xhat) MyCost(xhat,model); % Cost Function nVar=model.n*(model.n-1)/2; % Number of Decision Variables VarSize=[1 nVar]; % Decision Variables Matrix Size VarMin=0; % Lower Bound of Variables VarMax=1; % Upper Bound of Variables %% PSO Parameters MaxIt=250; % Maximum Number of Iterations nPop=250; % Population Size (Swarm Size) w=0.2; % Inertia Weight wdamp=1; % Inertia Weight Damping Ratio c1=0.7; % Personal Learning Coefficient c2=1.0; % Global Learning Coefficient % Constriction Coefficients % phi1=2.05; % phi2=2.05; % phi=phi1+phi2; % chi=2/(phi-2+sqrt(phi^2-4*phi)); % w=chi; % Inertia Weight % wdamp=1; % Inertia Weight Damping Ratio % c1=chi*phi1; % Personal Learning Coefficient % c2=chi*phi2; % Global Learning Coefficient % Velocity Limits VelMax=0.1*(VarMax-VarMin); VelMin=-VelMax; mu = 0.1; % Mutation Rate %% Initialization empty_particle.Position=[]; empty_particle.Cost=[]; empty_particle.Sol=[]; empty_particle.Velocity=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; empty_particle.Best.Sol=[]; particle=repmat(empty_particle,nPop,1); BestSol.Cost=inf; for i=1:nPop % Initialize Position particle(i).Position=unifrnd(VarMin,VarMax,VarSize); % Initialize Velocity particle(i).Velocity=zeros(VarSize); % Evaluation [particle(i).Cost, particle(i).Sol]=CostFunction(particle(i).Position); % Update Personal Best particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; particle(i).Best.Sol=particle(i).Sol; % Update Global Best if particle(i).Best.Cost<BestSol.Cost BestSol=particle(i).Best; end end BestCost=zeros(MaxIt,1); %% PSO Main Loop for it=1:MaxIt for i=1:nPop % Update Velocity particle(i).Velocity = w*particle(i).Velocity ... +c1*rand(VarSize).*(particle(i).Best.Position-particle(i).Position) ... +c2*rand(VarSize).*(BestSol.Position-particle(i).Position); % Apply Velocity Limits particle(i).Velocity = max(particle(i).Velocity,VelMin); particle(i).Velocity = min(particle(i).Velocity,VelMax); % Update Position particle(i).Position = particle(i).Position + particle(i).Velocity; % Velocity Mirror Effect IsOutside=(particle(i).Position<VarMin | particle(i).Position>VarMax); particle(i).Velocity(IsOutside)=-particle(i).Velocity(IsOutside); % Apply Position Limits particle(i).Position = max(particle(i).Position,VarMin); particle(i).Position = min(particle(i).Position,VarMax); % Evaluation [particle(i).Cost, particle(i).Sol] = CostFunction(particle(i).Position); % Mutation for k=1:2 NewParticle=particle(i); NewParticle.Position=Mutate(particle(i).Position, mu); [NewParticle.Cost, NewParticle.Sol]=CostFunction(NewParticle.Position); if NewParticle.Cost<=particle(i).Cost || rand < 0.1 particle(i)=NewParticle; end end % Update Personal Best if particle(i).Cost<particle(i).Best.Cost particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; particle(i).Best.Sol=particle(i).Sol; % Update Global Best if particle(i).Best.Cost<BestSol.Cost BestSol=particle(i).Best; end end end % Local Search based on Mutation for k=1:5 NewParticle=BestSol; NewParticle.Position=Mutate(BestSol.Position, mu); [NewParticle.Cost, NewParticle.Sol]=CostFunction(NewParticle.Position); if NewParticle.Cost<=BestSol.Cost BestSol=NewParticle; end end BestCost(it)=BestSol.Cost; disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); w=w*wdamp; % Plot Best Solution figure(1); PlotSolution(BestSol.Sol,model); pause(0.01); end %% Results figure; plot(BestCost,'LineWidth',2); xlabel('Iteration'); ylabel('Best Cost');