如何修改网站图片,工业设计专业作品集,茶叶企业网站开发源码,建设银行东营分行网站%%%%%%%%%%%%蚁群算法解决 TSP 问题%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%% clear all; %清除所有变量 close all; %清图 clc; %清屏 m 50; %蚂蚁个数 Alpha 1; %信息素重要程度参数 Beta 5; %启发式因子重要程度参数 Rho 0.1; %信息素蒸发系数 G 20…%%%%%%%%%%%%蚁群算法解决 TSP 问题%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%% clear all; %清除所有变量 close all; %清图 clc; %清屏 m 50; %蚂蚁个数 Alpha 1; %信息素重要程度参数 Beta 5; %启发式因子重要程度参数 Rho 0.1; %信息素蒸发系数 G 200; %最大迭代次数 Q 100; %信息素增加强度系数 C [1304 2312;3639 1315;4177 2244;3712 1399;3488 1535;3326 1556;... 3238 1229;4196 1044;4312 790;4386 570;3007 1970;2562 1756;... 2788 1491;2381 1676;1332 695;3715 1678;3918 2179;4061 2370;... 3780 2212;3676 2578;4029 2838;4263 2931;3429 1908;3507 2376;... 3394 2643;3439 3201;2935 3240;3140 3550;2545 2357;2778 2826;... 2370 2975]; %31 个省会城市坐标 %%%%%%%%%%%%%%%第一步变量初始化%%%%%%%%%%%%%% n size(C,1); %n 表示问题的规模(城市个数) D zeros(n,n); %D 表示两个城市距离间隔矩阵 for i 1:n for j 1:n if i ~ j D(i,j) ((C(i,1)-C(j,1))^2(C(i,2)-C(j,2))^2)^0.5; else D(i,j) eps; end D(j,i) D(i,j); end end Eta 1./D; %Eta 为启发因子这里设为距离的倒数 Tau ones(n,n); %Tau 为信息素矩阵 Tabu zeros(m,n); %存储并记录路径的生成 NC 1; %迭代计数器 R_best zeros(G,n); %各代最佳路线 L_best inf.*ones(G,1); %各代最佳路线的长度 figure(1); %优化解 while NC G %%%%%%%%%%第二步将 m 只蚂蚁放到 n 个城市上%%%%%%%%%% Randpos []; for i 1:(ceil(m/n)) Randpos [Randpos,randperm(n)]; end Tabu(:,1) (Randpos(1,1:m)); %%%第三步m 只蚂蚁按概率函数选择下一座城市完成各自的周游%%%% for j 2:n for i 1:m visited Tabu(i,1:(j-1)); %已访问的城市 J zeros(1,(n-j1)); %待访问的城市 P J; %待访问城市的选择概率分布 Jc 1; for k 1:n if length(find(visitedk))0 J(Jc) k; Jc Jc1; end end %%%%%%%%%%%计算待选城市的概率分布%%%%%%%%%%% for k 1:length(J) P(k) (Tau(visited(end),J(k))^Alpha)... *(Eta(visited(end),J(k))^Beta); end P P/(sum(P)); %%%%%%%%%%%按概率原则选取下一个城市%%%%%%%%% Pcum cumsum(P); Select find(Pcum rand); to_visit J(Select(1)); Tabu(i,j) to_visit; end end if NC 2 Tabu(1,:) R_best(NC-1,:); end %%%%%%%%%%%%%第四步记录本次迭代最佳路线%%%%%%%%%% L zeros(m,1); for i 1:m R Tabu(i,:); for j 1:(n-1) L(i) L(i)D(R(j),R(j1)); end L(i) L(i)D(R(1),R(n)); end L_best(NC) min(L); pos find(LL_best(NC)); R_best(NC,:) Tabu(pos(1),:); %%%%%%%%%%%%%%%第五步更新信息素%%%%%%%%%%%%% Delta_Tau zeros(n,n); for i 1:m for j 1:(n-1) Delta_Tau(Tabu(i,j),Tabu(i,j1)) ... Delta_Tau(Tabu(i,j),Tabu(i,j1))Q/L(i); end Delta_Tau(Tabu(i,n),Tabu(i,1)) ... Delta_Tau(Tabu(i,n),Tabu(i,1))Q/L(i); end Tau (1-Rho).*TauDelta_Tau; %%%%%%%%%%%%%%%第六步禁忌表清零%%%%%%%%%%%%% Tabu zeros(m,n); %%%%%%%%%%%%%%%%%历代最优路线%%%%%%%%%%%%%%% for i 1:n-1 plot([ C(R_best(NC,i),1), C(R_best(NC,i1),1)],... [C(R_best(NC,i),2), C(R_best(NC,i1),2)],bo-); hold on; end plot([C(R_best(NC,n),1), C(R_best(NC,1),1)],... [C(R_best(NC,n),2), C(R_best(NC,1),2)],ro-); title([优化最短距离:,num2str(L_best(NC))]); hold off; pause(0.005); NC NC1; end %%%%%%%%%%%%%%%%%第七步输出结果%%%%%%%%%%%%%% Pos find(L_bestmin(L_best)); Shortest_Route R_best(Pos(1),:); %最佳路线 Shortest_Length L_best(Pos(1)); %最佳路线长度 figure(2), plot(L_best) xlabel(迭代次数) ylabel(目标函数值) title(适应度进化曲线)
