PSO--Knapsack_Problem.rar_Knapsack Problem _knapsack matlab_prob

%PSO Approach For Solving Knapsack_Problem clear; clc; %************************************* seg(1)=0; success=0; PopSize=30; % Swarm Size dim=80; % Particle Size iter=0; ErrGoal=1e-14; w1=0.9; w2=0.4; MaxIt=4000; c1=2.0; c2=2.0; % initial swarm popul=rand(PopSize,dim)*(5.12+5.12); vel=rand(PopSize,dim); % mohasebe vazn for i=1:PopSize fit(i)=0; for j=1:dim fit(i)=fit(i)+popul(i,j).^2-10*cos(2*pi*popul(i,j))+10; end fit(i)=fit(i)+1; end % Global Best Position pbestx=popul; pbestfit=fit; [bestfar,g]=min(fit); gbestfit=bestfar; gbestx=popul(g,:); %******************************************* while(success==0)&&(iter<MaxIt) iter=iter+1; for i=1:PopSize % mohasebe vazn in Knapsack t(i)=gbestfit/pbestfit(i); if t(i)<1 if t(i)<0.0001 g=2; elseif t(i)>=0.0001&t(i)<0.01 g=1; elseif t(i)>=0.01&t(i)<0.1 g=0.5; elseif t(i)>=0.3&t(i)<0.9 g=0.3; elseif t(i)>=0.9&t(i)<1 g=0; end end w=w1*g+w2; %inertia weiindxht end for i=1:PopSize, A(i,:)=gbestx; end R1=rand(PopSize,dim); R2=rand(PopSize,dim); % Pso Approach vel=w*vel+c1*R1.*(pbestx-popul)+c2*R2.*(A-popul); popul=popul+vel; for i=1:PopSize for j=1:dim if popul(i,j)>5.12 popul(i,j)=5.12; end if popul(i,j)<-5.12 popul(i,j)=-5.12; end end end for i=1:PopSize fit(i)=0; for j=1:dim fit(i)=fit(i)+popul(i,j).^2-10*cos(2*pi*popul(i,j))+10; end fit(i)=fit(i)+1; end for i=1:PopSize if fit(i)<pbestfit(i) pbestfit(i)=fit(i); pbestx(i,:)=popul(i,:) end end [bestfar,g]=min(fit); if bestfar<gbestfit; gbestfit=bestfar; gbestx=popul(i,:) end seg(iter)=gbestfit; if abs(gbestfit-1)<=ErrGoal success=1; end end % end for %******************************************************************* % print result gbestx gbestfit iter plot(1:iter,seg(1:iter),'-r') xlabel('MaxIt'); ylabel('Value'); title('PSO Approach For Solving Knapsack Problem'); legend('Knapsack PSO');