pso.zip_optimization_粒子群算法

function out = PSO2(problem,params) %% Problem Defenition CostFunction =problem.CostFunction; %Cost Function nVar=problem.nVar; % Number of Unknown(decision) Variables (dimension) VarSize=[1 nVar]; %Matrix Size of Decision Variables VarMin =problem.VarMin; % Lower Bound of Decision Variables VarMax =problem.VarMax; % Upper Bound of Decision Variables %% Parameters of PSO 参数 MaxIt=params.MaxIt; %Maximum Number of Iterations nPop=params.nPop; %population Size (Swarm Size) w=params.w; %Intertia Coefficient wdamp=params.wdamp; %Damping Ratio of Inertia Weight c1=params.c1; c2=params.c2; %The Flag for Showing Iteration Information ShowIterInfo=params.ShowIterInfo; MaxVelocity=(VarMax-VarMin)*0.2; MinVelocity=-MaxVelocity; %% Initialiazation %The Particle Template empty_particle.Position=[]; empty_particle.Velocity=[]; empty_particle.Cost=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; %Creat Population Array particle=repmat(empty_particle,nPop,1); %Initialize Global Best GlobalBest.Cost=inf; %Initialize Population Members for i=1:nPop %Generate Random Solution particle(i).Position=unifrnd(VarMin,VarMax,VarSize); %Initialize Velocity particle(i).Velocity=zeros(VarSize); %Evaluation particle(i).Cost=CostFunction(particle(i).Position); %Update the Personal Best particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; %Update Global Best if particle(i).Best.Cost<GlobalBest.Cost GlobalBest=particle(i).Best; end end %Array to Hold Best Cost Value on Each Iteration BestCosts=zeros(MaxIt,1); %% Main Loop of PSO 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).*(GlobalBest.Position-particle(i).Position); %Apply Velocity Limits particle(i).Velocity=max(particle(i).Velocity,MinVelocity); particle(i).Velocity=min(particle(i).Velocity,MaxVelocity); %Update Position particle(i).Position=particle(i).Position+particle(i).Velocity; %Apply Lower and Upper Bound Limits particle(i).Position=max(particle(i).Position,VarMin); particle(i).Position=min(particle(i).Position,VarMax); %Evaluation particle(i).Cost=CostFunction(particle(i).Position); %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; %Update Global Best if particle(i).Best.Cost<GlobalBest.Cost GlobalBest=particle(i).Best; end end end %Store the Best Cost Value BestCosts(it)=GlobalBest.Cost; %Display Itreation Information if ShowIterInfo disp(['Iteration' num2str(it) ':Best Cost=' num2str(BestCosts(it))]); end % w=w*wdamp; end out.pop=particle; out.BestSol=GlobalBest; out.BestCosts=BestCosts; end