pso.m

修正过的pso 算法 属于离散型的pso程序

clear all
%aa=5;
%rand('state',aa);randn('state',aa);
datafile;                    %read data x and y
[n,n1]=size(data);
m=n1-1;                      %number of all x

%  ng=input('please input number of population in a generation:');
ng=100;
%mg=input('the max generations:');
mg=200;

xx=round(rand(ng,m));        %  population in a generation
xx((1:ng),n1)=1;

pp(1:ng,1:m)=xx(1:ng,1:m);   %  the history best site of every individual
fitmin(1:mg,1)=0;
fit(1:ng,1)=0;               %  the fitness of every individual




[fit]=fitness(data,xx);
ppfit=fit;
fitold=ppfit;
[fit1,nmin]=min(fit);
fitmin(1,1)=fit1;
minindividual(1,1:m)=xx(nmin,1:m);
   
   
for i=2:mg
   mutesite=rand(1,ng)<0.10;
   
   xx(nmin,1:m)=xx(nmin,1:m);
   
   index1=find(mutesite(1,:)==1);
   [a,b]=size(index1);
   xx(index1,1:m)=((xx(index1,1:m)+round(rand(b,m)))<2);
   
   index2=find(mutesite(1,:)~=1);
   [a,b]=size(index2);
   flag=rand(b,m);
   cc=ones(b,1)*minindividual(i-1,1:m);
   xx(index2,1:m)=(flag<=0.33).*xx(index2,1:m)+(flag>0.33&flag<0.67).*pp(index2,1:m)+(flag>=0.67).*cc;
   
   [fit]=fitness(data,xx);
   index3=find(fit(1,:)<fitold(1,:));
   index3=index3';
   ppfit(1,index3)=fit(1,index3);
   pp(index3,1:m)=xx(index3,1:m);
   fitold=ppfit;
   [fit1,nmin]=min(ppfit);
   fitmin(i,1)=fit1;
   minindividual(i,1:m)=pp(nmin,1:m);
end

         
index4=find(minindividual(mg,1:m)~=0);
variable=index4
fitmin1=min(fitmin) 
 plot(fitmin) ;