遗传算法程序代码--多目标优化--函数最值问题

("函数最值问题：F=X2+Y2-Z2,clearclc%%初始化pc=0.9;%交叉概率pm=0.05;%变异概率popsize=500;chromlength1=21;chromlength2=23;chromlength3=20;chromlength=chromlength1+chromlength2+chromlength3;pop=initpop(popsize,chromlength);%产生初始种群fori=1:500[objvalue]=calobjvalue(pop);%计算目标函数值[fitvalue]=calfitvalue(objvalue);%计算个体适应度[newpop]=selection(pop,fitvalue);%选择[newpop1]=crossover(newpop,pc);%交叉[newpop2]=mutation(newpop1,pm);%变异[newobjvalue]=newcalobjvalue(newpop2);%计算最新代目标函数值[newfitvalue]=newcalfitvalue(newobjvalue);%计算新种群适应度值[bestindividual,bestfit]=best(newpop2,newfitvalue);%求出群体中适应值最大的个体及其适应值y(i)=max(bestfit);%储存最优个体适应值pop5=bestindividual;%储存最优个体n(i)=i;%记录最优代位置%解码x1(i)=0+decodechrom(pop5,1,21)2/(pow2(21)-1);x2(i)=decodechrom(pop5,22,23)6/(pow2(23)-1)-1;x3(i)=decodechrom(pop5,45,20)1/(pow2(20)-1);pop=newpop2;end%%绘图figure(1)%最优点变化趋势图i=1:500;plot(y(i),'-b')xlabel('迭代次数');ylabel('最优个体适应值');title('最优点变化趋势');legend('最优点');gridon[zindex]=max(y);%计算最大值及其位置PO=n(index)%最优个体的位置X=x1(index)Y=x2(index)Z=x3(index)F=zfunction[bestindividual,bestfit]=best(newpop2,newfitvalue)%求出群体中最大得适应值及其个体%遗传算法子程序%Name:best.m[px,py]=size(newpop2);bestindividual=newpop2(1,:);bestfit=newfitvalue(1);fori=2:pxifnewfitvalue(i)>bestfitbestindividual=newpop2(i,:);bestfit=newfitvalue(i);endendfunction[fitvalue]=calfitvalue(objvalue)%计算个体的适应值%遗传算法子程序%Name:calfitvalue.mfitvalue=objvalue;function[objvalue]=calobjvalue(pop)%计算目标函数值%遗传算法子程序%Name:calobjvalue.mtemp1=decodechrom(pop,1,21);%将pop每行转化成十进制数相当于X'temp2=decodechrom(pop,22,23);temp3=decodechrom(pop,45,20);x1=temp12/(pow2(21)-1);%将二值域中的数转化为变量域的数相当于十进制的Xx2=temp26/(pow2(23)-1)-1;x3=temp31/(pow2(20)-1);objvalue=x1.^2+x2.^2-x3.^2;%计算目标函数值function[newpop1]=crossover(newpop,pc)%交叉%遗传算法子程序%Name:crossover.m[px,py]=size(newpop);newpop1=zeros(size(newpop));fori=1:2:px-1po=rand(1);ifpoms(newin)newpop(newin,:)=pop(fitin,:);newin=newin+1;elsefitin=fitin+1;endend",)