matlab代码 遗传算法解决tsp
时间: 2023-09-12 18:08:47 浏览: 37
以下是使用遗传算法解决TSP问题的MATLAB代码:
```matlab
% TSP遗传算法实现
clear all;
close all;
clc;
% 城市坐标数据
data = [0.4000 0.4439;
0.2439 0.1463;
0.1707 0.2293;
0.2293 0.7610;
0.5171 0.9414;
0.8732 0.6536;
0.6878 0.5219;
0.8488 0.3609;
0.6683 0.2536;
0.6195 0.2634];
% 坐标数据的个数
n = size(data, 1);
% 初始化种群
popSize = 200; % 种群大小
pop = zeros(popSize, n); % 种群矩阵
for i = 1 : popSize
pop(i, :) = randperm(n); % 随机产生一组个体
end
% 遗传算法参数设置
maxGen = 300; % 最大遗传代数
pc = 0.8; % 交叉概率
pm = 0.05; % 变异概率
bestDist = inf; % 最优路径长度
% 迭代遗传
for i = 1 : maxGen
% 适应度计算
dist = zeros(popSize, 1); % 路径长度向量
for j = 1 : popSize
dist(j) = 0; % 路径长度初始化为0
for k = 1 : n-1
dist(j) = dist(j) + norm(data(pop(j,k+1),:) - data(pop(j,k),:)); % 计算路径长度
end
dist(j) = dist(j) + norm(data(pop(j,1),:) - data(pop(j,n),:)); % 回到起点
end
[fitness, index] = min(dist); % 找到最短路径及其所在的个体
if fitness < bestDist % 更新最优路径长度及其对应的路径
bestDist = fitness;
bestPath = pop(index, :);
end
% 选择个体
fitness = dist.^-1; % 适应度
fitness = fitness / sum(fitness); % 归一化
cumFitness = cumsum(fitness); % 累计适应度
newPop = zeros(popSize, n); % 新种群矩阵
for j = 1 : popSize
p1 = find(cumFitness >= rand, 1); % 选择父代1
p2 = find(cumFitness >= rand, 1); % 选择父代2
% 交叉操作
if rand < pc
cutPoint1 = randi(n-1); % 随机选择交叉点1
cutPoint2 = randi(n-1); % 随机选择交叉点2
if cutPoint1 > cutPoint2 % 交叉点1和交叉点2交换
temp = cutPoint1;
cutPoint1 = cutPoint2;
cutPoint2 = temp;
end
newPop(j,:) = pop(p1,:);
newPop(j,cutPoint1:cutPoint2) = pop(p2,cutPoint1:cutPoint2);
% 变异操作
if rand < pm
mutatePoint1 = randi(n);
mutatePoint2 = randi(n);
temp = newPop(j,mutatePoint1);
newPop(j,mutatePoint1) = newPop(j,mutatePoint2);
newPop(j,mutatePoint2) = temp;
end
else % 不交叉则直接选择父代1
newPop(j,:) = pop(p1,:);
end
end
% 更新种群
pop = newPop;
end
% 输出结果
disp(['最短路径长度:', num2str(bestDist)]);
disp(['最优路径:', num2str(bestPath)]);
```
其中,`data`是城市坐标数据,`popSize`是种群大小,`pc`是交叉概率,`pm`是变异概率,`maxGen`是最大遗传代数。程序首先随机初始化种群,然后进行适应度计算,选择和交叉操作,最后更新种群。程序输出最短路径长度和最优路径。