大家好，我是考100分的小小码 ，祝大家学习进步，加薪顺利呀。今天说一说基于模拟退火算法的tsp问题_模拟退火算法解决旅行商问题,希望您对编程的造诣更进一步.
1 算法介绍
模型介绍见这里。
2 部分代码
%%%%%%%%%%%%%%%%%%%%%%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')
3 仿真结果
4 参考文献
[1]夏仁强. 多种群自适应模拟退火遗传算法求解TSP问题[J]. 毕节学院学报, 2008(04):82-86.
[2]郭晓利, 李航宇. 模拟退火遗传算法求解TSP问题[J]. 福建电脑, 2014, 000(005):15-16.
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