遗传算法tsp matlab代码
时间: 2023-09-04 19:09:38 浏览: 53
以下是一个基本的遗传算法TSP Matlab代码:
```matlab
% 遗传算法TSP Matlab代码
% 假设有一个城市列表存储在cities的矩阵中
% cities的每一行代表一个城市,包含两个元素:x和y坐标
% population_size表示种群大小,max_generations表示最大迭代次数
% 初始化种群
population = zeros(population_size, length(cities));
for i = 1:population_size
population(i,:) = randperm(length(cities));
end
% 计算初始种群的适应度
fitness = zeros(population_size, 1);
for i = 1:population_size
fitness(i) = calculate_fitness(population(i,:), cities);
end
% 迭代直到达到最大迭代次数
for generation = 1:max_generations
% 选择父代
parents = select_parents(population, fitness);
% 交叉
offspring = crossover(parents);
% 变异
offspring = mutate(offspring);
% 计算后代的适应度
offspring_fitness = zeros(size(offspring,1), 1);
for i = 1:size(offspring,1)
offspring_fitness(i) = calculate_fitness(offspring(i,:), cities);
end
% 合并父代和后代
combined_population = [population; offspring];
combined_fitness = [fitness; offspring_fitness];
% 选择下一代
[population, fitness] = select_next_generation(combined_population, combined_fitness, population_size);
end
% 找到最佳路线
[~, best_index] = min(fitness);
best_route = population(best_index,:);
best_distance = calculate_distance(best_route, cities);
% 绘制最佳路线
figure;
plot(cities(:,1), cities(:,2), 'o');
hold on;
plot([cities(best_route,1); cities(best_route(1),1)], [cities(best_route,2); cities(best_route(1),2)], '-o');
title(['Best Distance: ', num2str(best_distance)]);
```
其中,calculate_fitness函数计算一个个体(即城市列表)的适应度,crossover函数实现交叉,mutate函数实现变异,select_parents函数选择父代,select_next_generation函数选择下一代。
需要注意的是,这只是一个基本的遗传算法TSP Matlab代码,你可能需要根据具体的问题进行修改和优化。