matlab优化算法源码.zip

%%%%%%%%%%%%%%%f(X)=x+10sin(5X)+7cos(4x)%%%%%%%%%%%%%%%%%%%%%% % clear all; %清除所有变量 % close all; %清图 % clc; %清屏 % x=0:0.1:10; %x是一串等差数组来表示 % y=x+10*sin(5*x)+7*cos(4*x); %y是x的函数 % plot(x,y) % xlable('x') %绘制图像的x轴 % ylable('f(x)') %绘制图像的y轴 % title('f(x)=x+10sin(5X)+7cos(4x)') %%%%%%%%%%%%%%%参数部分%%%%%%%%%%%%%%%%%%%% clear all; %清除所有变量 close all; %清图 clc; %清屏 NP=50;%种群数量 L=20;%二进制位串长度 Pc=0.8;%交叉率 Pm=0.1; G=100;%最大遗传代数 Xs=10;%上限 Xx=0;%下限 f=randi([0,1],NP,L);%利用随机数，随机获取初始种群 %%%%%%%%%%%%%%%遗传算法循环%%%%%%%%%%%%%%%%% for k=1:G %%%%%%%%%%%%%%%%% for i=1:NP U=f(i,:); m=0; for j=1:L m=U(j)*2^(j-1)+m; end x(i)=Xx+m*(Xs-Xx)/(2^L-1); Fit(i)=funcl(x(i)); end maxFit=max(Fit); minFit=funcl(x(i)); rr=find(Fit==maxFit); fBest=f(rr(1,1),:); xBest=x(rr(1,1)); Fit=(Fit-minFit)/(maxFit-minFit); %%%%%%%%%%%%%%%%%%%% sum_Fit=sum(Fit); fitvalue=Fit./sum_Fit; fitvalue=cumsum(fitvalue); ms=sort(rand(NP,1)); fiti=1; newi=1; while newi<=NP if(ms(newi))<fitvalue(fiti) nf(newi,:)=f(fiti,:); newi=newi+1; else fiti=fiti+1; end end %%%%%%%%%%%%%%%%%% for i=1:2:NP p=rand; if p<Pc q=randi([0,1],1,L); for j=1:L-1 if q(j)==1; temp=nf(i+1,j); nf(i+1,j)=nf(i,j); nf(i,j)=temp; end end end end %%%%%%%%%%%%%%%%%%% i=1; while i<=round(NP*Pm) h=randi([1,NP],1,1); for j=1:round(L*Pm) g=randi([1,L],1,1); nf(h,g)=~nf(h,g); end i=i+1; end f=nf; f(i,:)=fBest; trace(k)=maxFit; end xBest; figure plot(trace) xlablel('迭代次数') ylablel('目标函数值') title('适应度进化曲线') %%%%%%%%%%%%%%%