PSO_TSP（混合粒子群：较优）.zip_改进蚁群_混合粒子群 tsp_蚁群混合算法_蚁群粒子群

%% 该文件演示基于TSP-PSO算法 clc;clear %% 下载数据 cityDist= cityCoor=[1:13;1:13]';%城市坐标矩阵 figure plot(cityCoor(:,1),cityCoor(:,2),'ms','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g') legend('城市位置') ylim([4 78]) title('城市分布图','fontsize',12) xlabel('km','fontsize',12) ylabel('km','fontsize',12) %ylim([min(cityCoor(:,2))-1 max(cityCoor(:,2))+1]) grid on %% 计算城市间距离 n=size(cityCoor,1); %城市数目 %cityDist=zeros(n,n); %城市距离矩阵 % cityDist=[0,12.9000000000000,20.8000000000000,28,35.7000000000000,22.1000000000000,30.2000000000000,6,11.9000000000000,16.3000000000000,23.7000000000000,27.8000000000000,36;12.9000000000000,0,7.90000000000000,15.1000000000000,22.8000000000000,9.20000000000000,17.3000000000000,18.9000000000000,21,8.80000000000000,16.2000000000000,20.3000000000000,28.5000000000000;20.8000000000000,7.90000000000000,0,7.20000000000000,14.9000000000000,17.1000000000000,25.2000000000000,26.8000000000000,28.9000000000000,16.7000000000000,24.1000000000000,28.2000000000000,36.4000000000000;28,15.1000000000000,7.20000000000000,0,7.70000000000000,24.3000000000000,18,34,36.1000000000000,23.9000000000000,31.3000000000000,35.4000000000000,32.9000000000000;35.7000000000000,22.8000000000000,14.9000000000000,7.70000000000000,0,18.4000000000000,10.3000000000000,41.7000000000000,43.8000000000000,31.6000000000000,25.7000000000000,33.4000000000000,25.2000000000000;22.1000000000000,9.20000000000000,17.1000000000000,24.3000000000000,18.4000000000000,0,8.10000000000000,25,26.9000000000000,14.7000000000000,7.30000000000000,26.2000000000000,23;30.2000000000000,17.3000000000000,25.2000000000000,18,10.3000000000000,8.10000000000000,0,33.1000000000000,35,22.8000000000000,15.4000000000000,23.1000000000000,14.9000000000000;6,18.9000000000000,26.8000000000000,34,41.7000000000000,25.0000000000000,33.1000000000000,0,5.90000000000000,10.3000000000000,17.7000000000000,21.8000000000000,30;11.9000000000000,21,28.9000000000000,36.1000000000000,43.8000000000000,26.9000000000000,35,5.90000000000000,0,12.2000000000000,19.6000000000000,23.7000000000000,31.9000000000000;16.3000000000000,8.80000000000000,16.7000000000000,23.9000000000000,31.6000000000000,14.7000000000000,22.8000000000000,10.3000000000000,12.2000000000000,0,7.40000000000000,11.5000000000000,19.7000000000000;23.7000000000000,16.2000000000000,24.1000000000000,31.3000000000000,25.7000000000000,7.30000000000000,15.4000000000000,17.7000000000000,19.6000000000000,7.40000000000000,0,18.9000000000000,20.3000000000000;27.8000000000000,20.3000000000000,28.2000000000000,35.4000000000000,33.4000000000000,26.2000000000000,23.1000000000000,21.8000000000000,23.7000000000000,11.5000000000000,18.9000000000000,0,8.20000000000000;36,28.5000000000000,36.4000000000000,32.9000000000000,25.2000000000000,23,14.9000000000000,30,31.9000000000000,19.7000000000000,20.3000000000000,8.20000000000000,0] nMax=200; %进化次数 indiNumber=1000; %个体数目 individual=zeros(indiNumber,n); %^初始化粒子位置 for i=1:indiNumber individual(i,:)=randperm(n); end %% 计算种群适应度 indiFit=fitness(individual,cityCoor,cityDist); [value,index]=min(indiFit); tourPbest=individual; %当前个体最优 tourGbest=individual(index,:) ; %当前全局最优 recordPbest=inf*ones(1,indiNumber); %个体最优记录 recordGbest=indiFit(index); %群体最优记录 xnew1=individual; %% 循环寻找最优路径 L_best=zeros(1,nMax); for N=1:nMax N %计算适应度值 indiFit=fitness(individual,cityCoor,cityDist); %更新当前最优和历史最优 for i=1:indiNumber if indiFit(i)<recordPbest(i) recordPbest(i)=indiFit(i); tourPbest(i,:)=individual(i,:); end if indiFit(i)<recordGbest recordGbest=indiFit(i); tourGbest=individual(i,:); end end [value,index]=min(recordPbest); recordGbest(N)=recordPbest(index); %% 交叉操作 for i=1:indiNumber % 与个体最优进行交叉 c1=unidrnd(n-1); %产生交叉位 c2=unidrnd(n-1); %产生交叉位 while c1==c2 c1=round(rand*(n-2))+1; c2=round(rand*(n-2))+1; end chb1=min(c1,c2); chb2=max(c1,c2); cros=tourPbest(i,chb1:chb2); ncros=size(cros,2); %删除与交叉区域相同元素 for j=1:ncros for k=1:n if xnew1(i,k)==cros(j) xnew1(i,k)=0; for t=1:n-k temp=xnew1(i,k+t-1); xnew1(i,k+t-1)=xnew1(i,k+t); xnew1(i,k+t)=temp; end end end end %插入交叉区域 xnew1(i,n-ncros+1:n)=cros; %新路径长度变短则接受 dist=0; for j=1:n-1 dist=dist+cityDist(xnew1(i,j),xnew1(i,j+1)); end dist=dist+cityDist(xnew1(i,1),xnew1(i,n)); if indiFit(i)>dist individual(i,:)=xnew1(i,:); end % 与全体最优进行交叉 c1=round(rand*(n-2))+1; %产生交叉位 c2=round(rand*(n-2))+1; %产生交叉位 while c1==c2 c1=round(rand*(n-2))+1; c2=round(rand*(n-2))+1; end chb1=min(c1,c2); chb2=max(c1,c2); cros=tourGbest(chb1:chb2); ncros=size(cros,2); %删除与交叉区域相同元素 for j=1:ncros for k=1:n if xnew1(i,k)==cros(j) xnew1(i,k)=0; for t=1:n-k temp=xnew1(i,k+t-1); xnew1(i,k+t-1)=xnew1(i,k+t); xnew1(i,k+t)=temp; end end end end %插入交叉区域 xnew1(i,n-ncros+1:n)=cros; %新路径长度变短则接受 dist=0; for j=1:n-1 dist=dist+cityDist(xnew1(i,j),xnew1(i,j+1)); end dist=dist+cityDist(xnew1(i,1),xnew1(i,n)); if indiFit(i)>dist individual(i,:)=xnew1(i,:); end %% 变异操作 c1=round(rand*(n-1))+1; %产生变异位 c2=round(rand*(n-1))+1; %产生变异位 while c1==c2 c1=round(rand*(n-2))+1; c2=round(rand*(n-2))+1; end temp=xnew1(i,c1); xnew1(i,c1)=xnew1(i,c2); xnew1(i,c2)=temp; %新路径长度变短则接受 dist=0; for j=1:n-1 dist=dist+cityDist(xnew1(i,j),xnew1(i,j+1)); end dist=dist+cityDist(xnew1(i,1),xnew1(i,n)); if indiFit(i)>dist individual(i,:)=xnew1(i,:); end end [value,index]=min(indiFit); L_best(N)=indiFit(index); tourGbest=individual(index,:); end %% 结果作图 figure plot(L_best) title('算法训练过程') xlabel('迭代次数') ylabel('适应度值') grid on figure hold on plot([cityCoor(tourGbest(1),1),cityCoor(tourGbest(n),1)],[cityCoor(tourGbest(1),2),... cityCoor(tourGbest(n),2)],'ms-','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g') hold on for i=2:n plot([cityCoor(tourGbest(i-1),1),cityCoor(tourGbest(i),1)],[cityCoor(tourGbest(i-1),2),... cityCoor(tourGbest(i),2)],'ms-','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g') hold on end legend('规划路径') scatter(cityCoor(:,1),cityCoor(:,2)); title('规划路径','fontsize',10) xlabel('km','fontsize',10) ylabel('km','fontsize',10) grid on ylim([4 80]) disp(['最短距离:' num2str(value)]); disp(['最短路径:' num2str([tourGbest tourGbest(1)])]);