PSO.rar_PSO_evolutionary

function PSO % Particle Swarm Optimization - PSO clear % Initializing variables popsize = 10; % Size of the swarm npar = 1; % Dimension of the problem maxit = 1000; % Maximum number of iterations c1 = 1; % cognitive parameter c2 = 4-c1; % social parameter C=1; % constriction factor % Initializing swarm and velocities par=rand(popsize,npar); % random population of continuous values vel = rand(popsize,npar); % random velocities % Evaluate initial population ff=6; cost=feval(ff,par); % calculates population cost using % ff minc(1)=min(cost); % min cost meanc(1)=mean(cost); % mean cost globalmin=minc(1); % initialize global minimum % Initialize local minimum for each particle localpar = par; % location of local minima localcost = cost; % cost of local minima % Finding best particle in initial population [globalcost,indx] = min(cost); globalpar=par(indx,:); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Start iterations iter = 0; % counter while iter < maxit iter = iter + 1; % update velocity = vel w=(maxit-iter)/maxit; %inertia weiindxht r1 = rand(popsize,npar); % random numbers r2 = rand(popsize,npar); % random numbers vel = C*(w*vel + c1 *r1.*(localpar-par) +c2*r2.*(ones(popsize,1)*globalpar-par)); % update particle positions par = par + vel; % updates particle position overlimit=par<=1; underlimit=par>=-1; par=par.*overlimit+not(overlimit); par=par.*underlimit; % Evaluate the new swarm cost = feval(ff,par); % evaluates cost of swarm % Updating the best local position for each particle bettercost = cost < localcost; localcost = localcost.*not(bettercost) +cost.*bettercost; localpar(find(bettercost),:) =par(find(bettercost),:); % Updating index g [temp, t] = min(localcost); if temp<globalcost globalpar=par(t,:); indx=t; globalcost=temp; end [iter globalpar globalcost] % print output each % iteration minc(iter+1)=min(cost); % min for this % iteration globalmin(iter+1)=globalcost; % best min so far meanc(iter+1)=mean(cost); % avg. cost for % this iteration end% while figure(1) iters=0:length(minc)-1; plot(iters,minc,'b',iters,meanc,'g',iters,globalmin,'r'); xlabel('generation');ylabel('cost'); text(0,minc(1),'best');text(1,minc(2),'population average') end %************************************************************************ %************************************************************************ % Test functions for optimization function f=feval(funnum,x) if funnum==1 %F1 f=abs(x)+cos(x); elseif funnum==2 %F2 f=abs(x)+sin(x); elseif funnum==3 %F3 f=x(:,1).^2+x(:,2).^2; elseif funnum==4 %F4 f=100*(x(:,2).^2-x(:,1)).^2+(1-x(:,1)).^2; elseif funnum==5 %F5 f(:,1)=sum(abs(x')-10*cos(sqrt(abs(10*x'))))'; elseif funnum==6 %F6 f=(x.^2+x).*cos(x); elseif funnum==7 %F7 f=x(:,1).*sin(4*x(:,1))+1.1*x(:,2).*sin(2*x(:,2)); elseif funnum==8 %F8 f=x(:,2).*sin(4*x(:,1))+1.1*x(:,1).*sin(2*x(:,2)); elseif funnum==9 %F9 f(:,1)=x(:,1).^4+2*x(:,2).^4+randn(length(x(:,1)),1); elseif funnum==10 %F10 f(:,1)=20+sum(x'.^2-10*cos(2*pi*x'))'; elseif funnum==11 %F11 f(:,1)=1+sum(abs(x').^2/4000)'-prod(cos(x'))'; elseif funnum==12 %F12 f(:,1)=.5+(sin(sqrt(x(:,1).^2+x(:,2).^2).^2)-.5)./(1+.1*(x(:,1).^2+x(:,2).^2)); elseif funnum==13 %F13 aa=x(:,1).^2+x(:,2).^2; bb=((x(:,1)+.5).^2+x(:,2).^2).^0.1; f(:,1)=aa.^0.25.*sin(30*bb).^2+abs(x(:,1))+abs(x(:,2)); elseif funnum==14 %F14 f(:,1)=besselj(0,x(:,1).^2+x(:,2).^2)+abs(1-x(:,1))/10+abs(1-x(:,2))/10; elseif funnum==15 %F15 f(:,1)=-exp(.2*sqrt((x(:,1)-1).^2+(x(:,2)-1).^2)+(cos(2*x(:,1))+sin(2*x(:,1)))); elseif funnum==16 %F16 f(:,1)=x(:,1).*sin(sqrt(abs(x(:,1)-(x(:,2)+9))))-(x(:,2)+9).*sin(sqrt(abs(x(:,2)+0.5*x(:,1)+9))); elseif funnum==17 %MOO function x=x+1; f(:,1)=(x(:,1)+x(:,2).^2+sqrt(x(:,3))+1./x(:,4))/8.5; f(:,2)=(1./x(:,1)+1./x(:,2)+x(:,3)+x(:,4))/6; end end