发布时间 : 星期三 文章仿真的多目标优化(蚁群算法在旅行商问题中的应用)更新完毕开始阅读418b3da6284ac850ad024256
tabu_list=zeros(m,n); end
%输出结果，绘制图形
position=find(length_best==min(length_best)); shortest_path=routh_best(position(1),:) shortest_length=length_best(position(1)) %绘制最短路径 figure(1)
set(gcf,'Name','Ant Colony Optimization——Figure of shortest_path','Color','r') N=length(shortest_path);
scatter(C(:,1),C(:,2),50,'filled'); hold on
plot([C(shortest_path(1),1),C(shortest_path(N),1)],[C(shortest_path(1),2),C(shortest_path(N),2)]) set(gca,'Color','g') hold on for i=2:N
plot([C(shortest_path(i-1),1),C(shortest_path(i),1)],[C(shortest_path(i-1),2),C(shortest_path(i),2)]) hold on end
%绘制每次循环最短路径长度和平均路径长度 figure(2)
set(gcf,'Name','Ant Colony Optimization——Figure of length_best and length_average','Color','r') plot(length_best,'r') set(gca,'Color','g') hold on
plot(length_average,'k')
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