蚁群tsp旅行商150城市问题

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  • 2022-04-20 20:04
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蚁群151城市tsp有matlab源代码
aco.rar
  • aco
  • eil51init.m
    1.1KB
  • AcatspEval.m
    220B
  • AcatspEval.asv
    220B
  • aca_ant_colony_system.m
    7.5KB
  • dists.m
    6.7KB
  • Select_for_aca.m
    499B
  • Select_for_aca.asv
    952B
  • aca_ant_colony_system.asv
    7.4KB
内容介绍
function [Alocation,Newbest,traceInfo]=aca_ant_colony_system %(InitOps); % 参考文献:Ant colony system a cooperative learning approach to the traveling salesman problem %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 蚁群算法初始化程序开始 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%% 问题定义:获取城市位置坐标、计算距离矩阵 %%%%%%%%% InitOps=[]; [Location,DistMatrix,Ncities,Bestx,Lengx] = eil51init(InitOps); %China31cityinit Kroa100init kroB150init d198init pr76init Eli51init close all; figure (5); hold on; minx=min(DistMatrix(:,1)); maxx=max(DistMatrix(:,1)); miny=min(DistMatrix(:,2)); maxy=max(DistMatrix(:,2)); minm=min(minx,miny); maxm=max(maxx,maxy); l=(maxm-minm)/100; for i=1:Ncities plot(Location(i,1),Location(i,2),'*b'); text (Location(i,1)+l,Location(i,2)+l,num2str(i)); end for i=1:Ncities-1 line([Location(Bestx(i),1),Location(Bestx(i+1),1)] , [Location(Bestx(i),2),Location(Bestx(i+1),2)]) ; end line([Location(Bestx(1),1),Location(Bestx(Ncities),1)] , [Location(Bestx(1),2),Location(Bestx(Ncities),2)]) ; grid on,title(['全局最优解路线图-',num2str(Lengx)]),xlabel('横坐标'),ylabel('纵坐标'); legend('城市位置'); hold off ; % 初始化随机发生器状态 rand('state',sum(100*clock)); % ================================================ % 使用最近邻法构造一个初始游历,并据此计算信息系初值 p=zeros(1,Ncities+1); p(1)=round(Ncities*rand+0.5);% p存储目前找到的所有城市的编号 i=p(1); count=2; while count <= Ncities NNdist= inf ;%NNdist存储目前找到的和当前城市距离最短的城市的距离 pp= i ;% i存储当前城市的编号 pp存储目前找到的城市编号 for j= 1: Ncities if (DistMatrix(i, j) < NNdist) & (j~=i) & ((j~=p) == ones(1,length(p))) % 目标城市的要求为--距离短、且不能是当前城市,也不能是以前已经走过的城市 NNdist= DistMatrix(i, j) ; pp= j ; end end p(count)=pp; i= pp ; count= count + 1 ; end p=AcatspEval(p,DistMatrix,Ncities); len=p(1,Ncities+1); Q0=1/(Ncities*len); %%%%%%%%%% 设定系统有关参数 %%%%%%%%%% MaxNc=2000;% 最大代数 A=1;% 信息素因子 B=2;% 启发信息因子 P1=0.1;% 局部挥发系数初值 P2=0.1;% 全局挥发系数初值 R0=0.9; %选择概率 M=10;% 蚂蚁数量 %%%%%%%%%% 初始化信息素、启发信息矩阵、确定蚂蚁最初位置及允许矩阵 %%%%%%%%%% Pheromone=Q0*ones(Ncities,Ncities);% 信息素初始矩阵; Heuristic=1./DistMatrix;% 启发信息初始矩阵 Temp=ones(1,Ncities); Heuristic=1./(1./Heuristic+diag(Temp)); RandL=round(rand(M,1)*Ncities+0.5);%蚂蚁最初位置 Alocation0=zeros(M,Ncities+1);% 存放M+1个蚂蚁游历的路径及长度矩阵初始化 Alocation0(:,1)=RandL; Allow0=repmat(1:Ncities,M,1);% 允许访问的城市矩阵初始化 for Ak=1:M Allow0(Ak,RandL(Ak))=0; end %%%%%%%%%% 运行参数初始化 %%%%%%%%%% Nc=1;% 第一代 Lbestdis=inf; Cbestdis=inf; Fnewbest=0; Alocation=Alocation0;% 存放个蚂蚁游历的路径及长度矩阵初始化 Allow=Allow0; % 允许矩阵赋初值 t1=clock; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 蚁群算法初始化程序结束 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 蚁群算法主循环开始 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% while(Nc<=MaxNc) % M个蚂蚁选择Ncities个城市 for Cityi=2:Ncities+1 if Cityi<Ncities+1 for Ak=1:M i=Alocation(Ak,Cityi-1);% 当前城市 j=Select_for_aca(R0,Ak,i,Allow,A,B,Pheromone,Heuristic);% 依据Pij选择下一个城市j Alocation(Ak,Cityi)=j; Allow(Ak,j)=0;% 更新允许矩阵 Pheromone(i,j)=(1-P1)*Pheromone(i,j)+P1*Q0; % 信息素在线单步更新 Pheromone(j,i)= Pheromone(i,j); end else % 返回出发城市 for Ak=1:M i=Alocation(Ak,Cityi-1);% 当前城市 j=Alocation(Ak,1); Pheromone(i,j)=(1-P1)*Pheromone(i,j)+P1*Q0; % 信息素在线单步更新 Pheromone(j,i)=Pheromone(i,j); end end end % 计算每个蚂蚁找到的路径的长度 for Ak=1:M Alocation(Ak,:)=AcatspEval(Alocation(Ak,:),DistMatrix,Ncities);% 计算适应值即每个蚂蚁找到的路径长度 end % 保存最优、最差解; t2=clock; t=etime(t2,t1); [Cbestdis,Am]=min(Alocation(1:M,Ncities+1)); Cbest=Alocation(Am,:); % 给输出变量赋值 traceInfo(Nc,1)=Nc; %current generation traceInfo(Nc,2)= Cbest(Ncities+1); %Best fittness traceInfo(Nc,3)=mean(Alocation(:,Ncities+1)); %Avg fittness traceInfo(Nc,4)=std(Alocation(:,Ncities+1)); %计算标准方差 if (Cbestdis<Lbestdis)||(Nc==1) Fnewbest=Fnewbest+1; Newbest(Fnewbest,1)=Nc; Newbest(Fnewbest,2)=t; Newbest(Fnewbest,3:Ncities+3)=Cbest; Lbest=Cbest; Lbestdis=Cbest(Ncities+1); end % 信息素离线全局更新 Iindex=Lbest(1:Ncities); Jindex=[Lbest(2:Ncities) Lbest(1)]; for k=1:Ncities Pheromone(Iindex(k),Jindex(k))=(1-P1)*Pheromone(Iindex(k),Jindex(k))+P2*(1./Lbest(Ncities+1));%对最优路径上的信息素增加 Pheromone(Jindex(k),Iindex(k))=Pheromone(Iindex(k),Jindex(k)); end RandL=Alocation(:,Ncities); Alocation=zeros(M,Ncities+1);% 存放M+1个蚂蚁游历的路径及长度矩阵初始化 Alocation(:,1)=RandL; Allow=repmat(1:Ncities,M,1);% 允许访问的城市矩阵初始化 for Ak=1:M Allow(Ak,RandL(Ak))=0; end Nc=Nc+1; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 蚁群算法主循环结束 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 蚁群算法输出模块开始 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 以图形方式显示运行结果开始 %% figure(1); hold on; plot ( traceInfo (: , 1) , traceInfo (: , 2) ) ; grid on,title('离线性能'),xlabel('代数'),ylabel('最优个体适应值'); legend('最优个体适应值'); hold off; figure(2); hold on; plot( traceInfo (: , 1) , traceInfo (: , 3) ) ; grid on,title('在线性能'),xlabel('代数'),ylabel('平均适应值'); legend('平均适应值'); hold off; figure(3); hold on; plot( Newbest (: , 1) , Newbest (: , Ncities+3) ) ; grid on,title('全局最优解变化'),xlabel('代数'),ylabel('全局最优解适应度'); legend('全局最优解适应度'); hold off; figure (4); Gbest=Newbest(Fnewbest,:); hold on; minx=min(DistMatrix(:,1)); maxx=max(DistMatrix(:,1)); miny=min(DistMatrix(:,2)); maxy=max(DistMatrix(:,2)); minn=min(minx,miny); maxx=max(maxx,maxy); l=(maxx-minn)/100; for i=1:Ncities plot(Location(i,1),Location(i,2),'*b'); text (Location(i,1)+l,Location(i,2)+l,num2str(i)); end for i=3:Ncities+1 line([Location(Gbest(i),1),Location(Gbest(i+1),1)] , [Location(Gbest(i),2),Location(Gbest(i+1),2)]) ; end line([Location(Gbest(3),1),Location(Gbest(Ncities+2),1)] , [Location(Gbest(3),2),Location(Gbest(Ncities+2),2)]) ; grid on,title(['全局最优解路线图-',num2str(Lbestdis)]),xlabel('横坐标'),ylabel('纵坐标'); legend('城市位置'); hold off ; %% 以图形方式显示运行结果开始 %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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