使用PSO的固定特征选择

% % Copyright (c) 2015, Yarpiz (www.yarpiz.com) % All rights reserved. Please read the "license.txt" for license terms. % % Project Code: YPML122 % Project Title: Feature Selection using PSO (Fixed Number of Features) % Publisher: Yarpiz (www.yarpiz.com) % % Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team) % % Contact Info: sm.kalami@gmail.com, info@yarpiz.com % clc; clear; close all; %% Problem Definition data=LoadData(); nf=4; CostFunction=@(u) FeatureSelectionCost(u,nf,data); % Cost Function nVar=data.nx; % Number of Decision Variables VarSize=[1 nVar]; % Size of Decision Variables Matrix VarMin=0; % Lower Bound of Variables VarMax=1; % Upper Bound of Variables %% PSO Parameters MaxIt=20; % Maximum Number of Iterations nPop=20; % Population Size (Swarm Size) % w=1; % Inertia Weight % wdamp=0.99; % Inertia Weight Damping Ratio % c1=2; % Personal Learning Coefficient % c2=2; % 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; %% Initialization empty_particle.Position=[]; empty_particle.Cost=[]; empty_particle.Out=[]; empty_particle.Velocity=[]; empty_particle.Best.Position=[]; empty_particle.Best.Cost=[]; empty_particle.Best.Out=[]; 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).Out]=CostFunction(particle(i).Position); % Update Personal Best particle(i).Best.Position=particle(i).Position; particle(i).Best.Cost=particle(i).Cost; particle(i).Best.Out=particle(i).Out; % 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).Out] = 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; particle(i).Best.Out=particle(i).Out; % Update Global Best if particle(i).Best.Cost<BestSol.Cost BestSol=particle(i).Best; end end end BestCost(it)=BestSol.Cost; disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); w=w*wdamp; end %% Results figure; plot(BestCost,'LineWidth',2); xlabel('Iteration'); ylabel('Best Cost');