GA.rar_GA_GA 二进制编码_encode genetic_extremum seeking_遗传求极值

%GA(Generic Algorithm) to Optimize Online PID Control clear all; close all; Size=120; CodeL=2; MinX(1)=9*ones(1);MaxX(1)=12*ones(1); MinX(2)=0.20*ones(1);MaxX(2)=0.30*ones(1); Kpid(:,1)=MinX(1)+(MaxX(1)-MinX(1))*rand(Size,1); Kpid(:,2)=MinX(2)+(MaxX(2)-MinX(2))*rand(Size,1); BsJ=0; J=0; x=zeros(1,2); xi=zeros(1,2); xk=zeros(1,2); ts=0.001; error_1=0; BestS=zeros(2,1); for k=1:1:100 time(k)=k*ts; rin(k)=1; %u(k)=10*x(1)+0.2*x(2); %Test PI: good results u(k)=BestS(1)*x(1)+BestS(2)*x(2); para=u(k); tSpan=[0 ts]; [tt,xx]=ode45('chap5_5f',tSpan,xk,[],para); xk=xx(length(xx),:); yout(k)=xk(1); error(k)=rin(k)-yout(k); x(1)=error(k); % Calculating P x(2)=(error(k)-error_1)/ts; % Calculating D error_1=error(k); B=0; M=1; %Using GA if M==1 G=10; for kg=1:1:G %Era evolution %****** Step 1 : Evaluate BestJ ****** for i=1:1:Size Kpidi=Kpid(i,:); ui(i)=Kpidi(1)*x(1)+Kpidi(2)*x(2); para=ui(i); [tt,xxi]=ode45('chap5_5f',tSpan,xk,[],para); y(i)=xxi(length(xxi),1); errori(i)=rin(k)-y(i); du(i)=ui(i)-u(k); de(i)=(errori(i)-error(k))/ts; alfap=0.95; betap=0; if abs(error(k))<=0.50 betap=0.05; end J=alfap*abs(errori(i))+betap*abs(de(i)); B=J; if errori(i)<0 B=B+100*abs(errori(i)); end BsJi(i)=B; [OderJi,IndexJi]=sort(BsJi); BestJ(kg)=OderJi(1); BJ=BestJ(kg); Ji=BsJi+1e-10; %Avoiding deviding zero fi=1./Ji; %Cm=max(Ji); %fi=Cm-Ji; end %End of a Size!!!!!!!!!! [Oderfi,Indexfi]=sort(fi); %Arranging fi small to bigger Bestfi=Oderfi(Size); %Let Bestfi=max(fi) BestS=Kpid(Indexfi(Size),:); %Let BestS=E(m), m is the Indexfi belong to max(fi) %****** Step 2 : Select and Reproduct Operation****** fi_sum=sum(fi); fi_Size=(Oderfi/fi_sum)*Size; fi_S=floor(fi_Size); % Selecting Bigger fi value r=Size-sum(fi_S); Rest=fi_Size-fi_S; [RestValue,Index]=sort(Rest); for i=Size:-1:Size-r+1 fi_S(Index(i))=fi_S(Index(i))+1; % Adding rest to equal Size end kr=1; for i=Size:-1:1 % Select the Sizeth and Reproduce firstly for j=1:1:fi_S(i) TempE(kr,:)=Kpid(Indexfi(i),:); % Select and Reproduce kr=kr+1; % kr is used to reproduce end end %************ Step 3 : Crossover Operation ************ Pc=0.90; for i=1:2:(Size-1) temp=rand; if Pc>temp %Crossover Condition alfa=rand; TempE(i,:)=alfa*Kpid(i+1,:)+(1-alfa)*Kpid(i,:); TempE(i+1,:)=alfa*Kpid(i,:)+(1-alfa)*Kpid(i+1,:); end end TempE(Size,:)=BestS; Kpid=TempE; %************ Step 4: Mutation Operation ************** Pm=0.20-[1:1:Size]*(0.01)/Size; %Bigger fi,smaller Pm 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) %Mutation Condition TempE(i,j)=Mean(j)+Dif(j)*(rand-0.5); end end end %Guarantee TempE(Size,:) belong to the best individual TempE(Size,:)=BestS; Kpid=TempE; end %End of kg kph(k)=BestS(1); kdh(k)=BestS(2); BestS; end %End of M=1 end %End of k figure(1); plot(time,rin,'r',time,yout,'b'); xlabel('Time(s)');ylabel('rin,yout'); figure(2); plot(time,u,'r'); xlabel('Time(s)');ylabel('u'); if M==1 figure(3); plot(time,kph,'r'); xlabel('Time(s)');ylabel('kp change'); figure(4); plot(time,kdh,'r'); xlabel('Time(s)');ylabel('kd change'); end