关于旅行商问题代码

%粒子群算法求解旅行商问题%BylReijcloseall;clearall;PopSize=500;%种群大小CityNum=14;%城市数OldBestFitness=0;%旧的最优适应度值Iteration=0;%迭代次数MaxIteration=2000;%最大迭代次数IsStop=0;%程序停止标志Num=0;%取得相同适应度值的迭代次数c1=0.5;%认知系数c2=0.7;%社会学习系数w=0.96-Iteration/MaxIteration;%惯性系数,随迭代次数增加而递减%节点坐标node=[16.4796.10;16.4794.44;20.0992.54;22.3993.37;25.2397.24;...22.0096.05;20.4797.02;17.2096.29;16.3097.38;14.0598.12;...16.5397.38;21.5295.59;19.4197.13;20.0994.55];%初始化各粒子,即产生路径种群Group=ones(CityNum,PopSize);fori=1:PopSizeGroup(:,i)=randperm(CityNum)';endGroup=Arrange(Group);%初始化粒子速度(即交换序)Velocity=zeros(CityNum,PopSize);fori=1:PopSizeVelocity(:,i)=round(rand(1,CityNum)'*CityNum);%round取整end%计算每个城市之间的距离CityBetweenDistance=zeros(CityNum,CityNum);fori=1:CityNumforj=1:CityNumCityBetweenDistance(i,j)=sqrt((node(i,1)-node(j,1))^2+(node(i,2)-node(j,2))^2);endend%计算每条路径的距离fori=1:PopSizeEachPathDis(i)=PathDistance(Group(:,i)',CityBetweenDistance);endIndivdualBest=Group;%记录各粒子的个体极值点位置,即个体找到的最短路径IndivdualBestFitness=EachPathDis;%记录最佳适应度值,即个体找到的最短路径的长度[GlobalBestFitness,index]=min(EachPathDis);%找出全局最优值和相应序号%初始随机解figure;subplot(2,2,1);PathPlot(node,CityNum,index,IndivdualBest);title('随机解');%寻优while(IsStop==0)&(Iteration<MaxIteration)%迭代次数递增Iteration=Iteration+1;%更新全局极值点位置,这里指路径fori=1:PopSizeGlobalBest(:,i)=Group(:,index);end%求pij-xij,pgj-xij交换序,并以概率c1,c2的保留交换序pij_xij=GenerateChangeNums(Group,IndivdualBest);pij_xij=HoldByOdds(pij_xij,c1);pgj_xij=GenerateChangeNums(Group,GlobalBest);pgj_xij=HoldByOdds(pgj_xij,c2);%以概率w保留上一代交换序Velocity=HoldByOdds(Velocity,w);Group=PathExchange(Group,Velocity);%根据交换序进行路径交换Group=PathExchange(Group,pij_xij);Group=PathExchange(Group,pgj_xij);fori=1:PopSize%更新各路径总距离EachPathDis(i)=PathDistance(Group(:,i)',CityBetweenDistance);endIsChange=EachPathDis<IndivdualBestFitness;%更新后的距离优于更新前的,记录序号IndivdualBest(:,find(IsChange))=Group(:,find(IsChange));%更新个体最佳路径IndivdualBestFitness=IndivdualBestFitness.*(~IsChange)+EachPathDis.*IsChange;%更新个体最佳路径距离[GlobalBestFitness,index]=min(EachPathDis);%更新全局最佳路径,记录相应的序号ifGlobalBestFitness==OldBestFitness%比较更新前和更新后的适应度值;Num=Num+1;%相等时记录加一;elseOldBestFitness=GlobalBestFitness;%不相等时更新适应度值,并记录清零;Num=0;endifNum>=20%多次迭代的适应度值相近时程序停止IsStop=1;endBestFitness(Iteration)=GlobalBestFitness;%每一代的最优适应度end%最优解subplot(2,2,2);PathPlot(node,CityNum,index,IndivdualBest);title('优化解');%进化曲线subplot(2,2,3);plot((1:Iteration),BestFitness(1:Iteration));gridon;title('进化曲线');%最小路径值GlobalBestFitnessfunctionGroup=Arrange(Group)[xy]=size(Group);[NO1,index]=min(Group',[],2);%找到最小值1fori=1:ypop=Group(:,i);temp1=pop([1:index(i)-1]);temp2=pop([index(i):x]);Group(:,i)=[temp2'temp1']';endfunctionChangeNums=GenerateChangeNums(Group,BestVar);[xy]=size(Group);ChangeNums=zeros(x,y);fori=1:ypop=BestVar(:,i);%从BestVar取出一个顺序pop1=Group(:,i);%从粒子群中取出对应的顺序forj=1:x%从BestVar的顺序中取出一个序号NoFromBestVar=pop(j);fork=1:x%从对应的粒子顺序中取出一个序号NoFromGroup=pop1(k);if(NoFromBestVar==NoFromGroup)&&(j~=k)%两序号同且不在同一位置ChangeNums(j,i)=k;%交换子pop1(k)=pop1(j);pop1(j)=NoFromGroup;endendendendfunctionHold=HoldByOdds(Hold,Odds)[x,y]=size(Hold);fori=1:xforj=1:yifrand>OddsHold(i,j)=0;endendendfunctionSumDistance=PathDistance(path,CityBetweenDistance)L=length(path);%path为一个循环的节点顺序SumDistance=0;fori=1:L-1SumDistance=SumDistance+CityBetweenDistance(path(i),path(i+1));endSumDistance=SumDistance+CityBetweenDistance(path(1),path(L));%加上首尾节点的距离functionGroup=PathExchange(Group,Index)[xy]=size(Group);fori=1:ya=Index(:,i);%取出其中一组交换序pop=Group(:,i);%取出对应的粒子forj=1:x%取出其中一个交换算子作交换ifa(j)~=0pop1=pop(j);pop(j)=pop(a(j));pop(a(j))=pop1;endendGroup(:,i)=pop;endfunctionPathPlot(