【路径规划-TSP问题】基于模拟退火算法求解31城市旅行商问题Matlab源码 上传.zip

%%%%%%%%%%%%%%%%%%%%%%模拟退火算法解决TSP问题%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear all; %清除所有变量 close all; %清图 clc; %清屏 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 n=size(C,1); %TSP问题的规模,即城市数目 T=100*n; %初始温度 L=100; %马可夫链长度 K=0.99; %衰减参数 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%城市坐标结构体%%%%%%%%%%%%%%%%%%%%%%%%%% city=struct([]); for i=1:n city(i).x=C(i,1); city(i).y=C(i,2); end l=1; %统计迭代次数 len(l)=func3(city,n); %每次迭代后的路线长度 figure(1); while T>0.001 %停止迭代温度 %%%%%%%%%%%%%%%%多次迭代扰动，温度降低之前多次实验%%%%%%%%%%%%%%% for i=1:L %%%%%%%%%%%%%%%%%%%计算原路线总距离%%%%%%%%%%%%%%%%%%%%%%%%% len1=func3(city,n); %%%%%%%%%%%%%%%%%%%%%%%%%产生随机扰动%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%随机置换两个不同的城市的坐标%%%%%%%%%%%%%%%%% p1=floor(1+n*rand()); p2=floor(1+n*rand()); while p1==p2 p1=floor(1+n*rand()); p2=floor(1+n*rand()); end tmp_city=city; tmp=tmp_city(p1); tmp_city(p1)=tmp_city(p2); tmp_city(p2)=tmp; %%%%%%%%%%%%%%%%%%%%%%%%计算新路线总距离%%%%%%%%%%%%%%%%%%%% len2=func3(tmp_city,n); %%%%%%%%%%%%%%%%%%新老距离的差值，相当于能量%%%%%%%%%%%%%%%%% delta_e=len2-len1; %%%%%%%%%%%%新路线好于旧路线，用新路线代替旧路线%%%%%%%%%%%%%% if delta_e<0 city=tmp_city; else %%%%%%%%%%%%%%%%%%以概率选择是否接受新解%%%%%%%%%%%%%%%%% if exp(-delta_e/T)>rand() city=tmp_city; end end end l=l+1; %%%%%%%%%%%%%%%%%%%%%%%%%计算新路线距离%%%%%%%%%%%%%%%%%%%%%%%%%% len(l)=func3(city,n); %%%%%%%%%%%%%%%%%%%%%%%%%%%温度不断下降%%%%%%%%%%%%%%%%%%%%%%%%%% T=T*K; for i=1:n-1 plot([city(i).x,city(i+1).x],[city(i).y,city(i+1).y],'bo-'); hold on; end plot([city(n).x,city(1).x],[city(n).y,city(1).y],'ro-'); title(['优化最短距离:',num2str(len(l))]); hold off; pause(0.005); end figure(2); plot(len) xlabel('迭代次数') ylabel('目标函数值') title('适应度进化曲线')