使用基本遗传算法和非支配排序算法求解了一个车辆充电调度系统，.rar

function Copy_of_NSGAII() clc;format compact;tic;hold on global fitvalue; global fitvalue2; global fitvalue3; global fitvalue4; global newpop; global pop; global newpop_mut; global Best_individual; %---初始化/参数设定clear all generations=100; %迭代次数 popnum=100; %种群大小(须为偶数) M=8; %个体长度 N=5; cross_rate=0.95; mutate_rate=0.03; % minvalue=repmat(zeros(1,poplength),popnum,1); %个体最小值 % maxvalue=repmat(ones(1,poplength),popnum,1); %个体最大值 initpop(popnum,M,N); % 初始化,产生新的初始种群 %---开始迭代进化 for gene=1:generations %开始 迭代 %-------交叉 crossover(popnum,M,N, cross_rate);% 交叉操作 % newpopulation=zeros(popnum,poplength); %子代种群 % for i=1:popnum/2 %交叉产生子代 % k=randperm(popnum); %从种群中随机选出两个父母,不采用二进制联赛方法 % beta=(-1).^round(rand(1,poplength)).*abs(randn(1,poplength))*1.481; %采用正态分布交叉产生两个子代 % newpopulation(i*2-1,:)=(population(k(1),:)+population(k(2),:))/2+beta.*(population(k(1),:)-population(k(2),:))./2; %产生第一个子代 % newpopulation(i*2,:)=(population(k(1),:)+population(k(2),:))/2-beta.*(population(k(1),:)-population(k(2),:))./2; %产生第二个子代 % end %-------变异 mutation(popnum,M,N, mutate_rate);% 变异操作 % k=rand(size(newpopulation)); %随机选定要变异的基因位 % miu=rand(size(newpopulation)); %采用多项式变异 % temp=k<1/poplength & miu<0.5; %要变异的基因位 % newpopulation(temp)=newpopulation(temp)+(maxvalue(temp)-minvalue(temp)).*((2.*miu(temp)+(1-2.*miu(temp)).*(1-(newpopulation(temp)-minvalue(temp))./(maxvalue(temp)-minvalue(temp))).^21).^(1/21)-1); %变异情况一 % newpopulation(temp)=newpopulation(temp)+(maxvalue(temp)-minvalue(temp)).*(1-(2.*(1-miu(temp))+2.*(miu(temp)-0.5).*(1-(maxvalue(temp)-newpopulation(temp))./(maxvalue(temp)-minvalue(temp))).^21).^(1/21)); %变异情况二 %-------越界处理/种群合并 newpop=[pop newpop_mut]; % newpopulation(newpopulation>maxvalue)=maxvalue(newpopulation>maxvalue); %子代越上界处理 % newpopulation(newpopulation<minvalue)=minvalue(newpopulation<minvalue); %子代越下界处理 % newpopulation=[population;newpopulation]; %合并父子种群 %-------计算目标函数值 index_1(newpop,M,N);%对个体的行驶时间和充电时间排序 wait(newpop,M,N); fitness(newpop, M,N); fitness2(newpop, M,N); fitness3(newpop, M,N); fitness4(newpop, M,N); functionvalue=zeros(size(newpop,2),4); %合并后种群的各目标函数值,这里的问题是ZDT1 functionvalue(:,1)=fitvalue(1:size(newpop,2)); functionvalue(:,2)=fitvalue2(1:size(newpop,2)); functionvalue(:,3)=fitvalue3(1:size(newpop,2)); functionvalue(:,4)=fitvalue4(1:size(newpop,2)); % functionvalue=zeros(size(newpopulation,1),2); %合并后种群的各目标函数值,这里的问题是ZDT1 % functionvalue(:,1)=newpopulation(:,1); %计算第一维目标函数值 % g=1+9*sum(newpopulation(:,2:poplength),2)./(poplength-1); % functionvalue(:,2)=g.*(1-(newpopulation(:,1)./g).^0.5); %计算第二维目标函数值 %-------非支配排序 fnum=0; %当前分配的前沿面编号 cz=false(1,size(functionvalue,1)); %记录个体是否已被分配编号 frontvalue=zeros(size(cz)); %每个个体的前沿面编号 [functionvalue_sorted,newsite]=sortrows(functionvalue); %对种群按第一维目标值大小进行排序 while ~all(cz) %开始迭代判断每个个体的前沿面,采用改进的deductive sort fnum=fnum+1; d=cz; for i=1:size(functionvalue,1) if ~d(i) for j=i+1:size(functionvalue,1) if ~d(j) k=1; for m=2:size(functionvalue,2) if functionvalue_sorted(i,m)>functionvalue_sorted(j,m) k=0; break end end if k d(j)=true; end end end frontvalue(newsite(i))=fnum; cz(i)=true; end end end %-------计算拥挤距离/选出下一代个体 fnum=0; %当前前沿面 while numel(frontvalue,frontvalue<=fnum+1)<=popnum % numel(frontvalue,frontvalue<=fnum+1)<=popnum %判断前多少个面的个体能完全放入下一代种群 fnum=fnum+1; end newnum=numel(frontvalue,frontvalue<=fnum); %前fnum个面的个体数 h=1;l=1; endpop=cell(1,500); for j=1:popnum*2 if frontvalue(j)==1 endpop{1,l}=newpop{1,j}; l=l+1; end if frontvalue(j)<=fnum pop{1,h}=newpop{1,j}; h=h+1; end end % pop{1,1:newnum}=newpop{1,frontvalue<=fnum}; % population(1:newnum,:)=newpopulation(frontvalue<=fnum,:); %将前fnum个面的个体复制入下一代 popu=find(frontvalue==fnum+1); %popu记录第fnum+1个面上的个体编号 distancevalue=zeros(size(popu)); %popu各个体的拥挤距离 fmax=max(functionvalue(popu,:),[],1); %popu每维上的最大值 fmin=min(functionvalue(popu,:),[],1); %popu每维上的最小值 for i=1:size(functionvalue,2) %分目标计算每个目标上popu各个体的拥挤距离 [~,newsite]=sortrows(functionvalue(popu,i)); distancevalue(newsite(1))=inf; distancevalue(newsite(end))=inf; for j=2:length(popu)-1 distancevalue(newsite(j))=distancevalue(newsite(j))+(functionvalue(popu(newsite(j+1)),i)-functionvalue(popu(newsite(j-1)),i))/(fmax(i)-fmin(i)); end end popu=-sortrows(-[distancevalue;popu]')'; %按拥挤距离降序排序第fnum+1个面上的个体 for i=newnum+1:popnum pop{1,i}=newpop{1,popu(2,1:popnum-newnum)}; end newpop=cell(1,popnum); for k=1:popnum newpop{1,k}=zeros(M,N); end % pop{newnum+1:popnum,:}=newpop{popu(2,1:popnum-newnum),:}; %将第fnum+1个面上拥挤距离较大的前popnum-newnum个个体复制入下一代 end %---程序输出 fprintf('已完成,耗时%4s秒\n',num2str(toc)); %程序最终耗时 output=sortrows(functionvalue(frontvalue==1,:)); %最终结果:种群中非支配解的函数值 for k=1:size(output,1) fit(k) = 0; end for k=1:size(output,1) fit(k)=0.2474*((output(k,1)-12.668)/15.198)^2+0.2445*((output(k,2)-221.13)/202.6456)^2+0.2544*((output(k,3)-0.0345)/3.9655)^2+0.2536*((output(k,4)-50)/10)^2; end x=1:1:size(output,1); y=fit(x); p=find(y==min(y)); plot(x,y,'*b'); text(x(p),y(p),'o','color','g') Best_individual=endpop{1,x(p)}; distribution_result(M,N) end