基于粒子群优化算法的p-Hub选址优化含Matlab代码.zip

clc; clear; close all; %% Problem Definition load('phlap_20.mat'); % Load Model CostFunction=@(xhat) MyCost(xhat,model); % Cost Function VarSize=[model.N model.N]; % Decision Variables Matrix Size nVar=prod(VarSize); % Number of Decision Variables VarMin=0; % Lower Bound of Decision Variables VarMax=1; % Upper Bound of Decision Variables %% PSO Parameters MaxIt=250; % Maximum Number of Iterations nPop=150; % Population Size (Swarm Size) w=1; % Inertia Weight wdamp=0.99; % Inertia Weight Damping Ratio c1=1.5; % Personal Learning Coefficient c2=2.0; % Global Learning Coefficient % Velocity Limits VelMax=0.1*(VarMax-VarMin); VelMin=-VelMax; %% 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 NewParticle=particle(i); NewParticle.Position=Mutate(particle(i).Position, model); [NewParticle.Cost, NewParticle.Sol]=CostFunction(NewParticle.Position); if NewParticle.Cost<=particle(i).Cost || rand < 0.1 particle(i)=NewParticle; 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=1:3 NewParticle=BestSol; NewParticle.Position=Mutate(BestSol.Position, model); [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');