yichuan.m

关于遗传算法在MATLAB中对典型二阶对象进行仿真

clear all;
clear all;
global rin yout timef

Size=30;
CodeL=3;

MinX(1)=zeros(1);
MaxX(1)=20*ones(1);
MinX(2)=zeros(1);
MaxX(2)=1.0*ones(1);
MinX(3)=zeros(1);
MaxX(3)=1.0*ones(1);

Kpid(:,1)=MinX(1)+(MaxX(1)-MinX(1))*rand(Size,1);
Kpid(:,2)=MinX(2)+(MaxX(2)-MinX(2))*rand(Size,1);
Kpid(:,3)=MinX(3)+(MaxX(3)-MinX(3))*rand(Size,1);

G=100;
BsJ=0;

for kg=1:1:G
    time(kg)=kg;

for i=1:1:Size
Kpidi=Kpid(i,:);
[Kpidi,BsJ]=chap5_3f(Kpidi,BsJ);
BsJi(i)=BsJ;
end

[OderJi,IndexJi]=sort(BsJi);
BestJ(kg)=OderJi(i);


fi=1./Ji;
Cm=max(Ji);
fi=Cm-Ji;

[Oderfi,Indexfi]=sort(fi);
Bestfi=Oderfi(Size);
BestS=Kpid(Indexfi(Size),:);
kg
BJ
BestS

fi_sum=sum(fi);
fi_Size=(Oderfi/fi_sum)*Size;

fi_S=floor(fi_Size);

[RestValue,Index]=sort(Rest);

for i=Size:-1:Size-r+1
    fi_S(Index(i))=fi_S(Index(i))+1;
end

k=1;
for i=Size:-1:1
    for j=1:1:fi_S(i);
        TempE(k,:)=Kpid(Indexfi(i),:);
        k=k+1;
    end
end

Pc=0.90;
for i=1:2:(Size-1);
    temp=rand;
    if Pc>temp
        alfa=rand;
        Temp(i,:)=alfa*Kpid(i+1,:)+(1-alfa)*Kpid(i,:);
        Temp(i+1,:)=alfa*Kpid(i,:)+(1-alfa)*Kpid(i+1,:);
    end
end
TempE(Size,:)=BestS;
Kpid=TempE;

Pm=0.10-[1:1:Size]*(0.01)/Size;
Pm_rand=rand(Size,CodeL);
Mean=(MaxX+MinX)/2;
Dif=(MaxX-MinX);

for i=1:1:Size;
   for j=1:1:CodeL
       if Pm(i)>Pm_rand(i,j)
           TempE(i,j)=Mean(j)+Dif(j)*(rand-0.5);
       end
   end
end

TempE(Size,:)=BestS;
Kpid=TempE;
end
Bestfi
BestS
Best_J=BestJ(G)
figure(1);
plot(time,BestJ);
xlabel('Times');ylabel('Best J');
figure(2);
plot(timef,rin,'r',timef,yout,'b');
xlabel('Time(s)');ylabel('rin,yout');