matlab遗传算法最小路径

以下是使用Matlab实现遗传算法求解最小路径的示例代码：

% 遗传算法求解最小路径
% 假设有5个城市，求解它们之间的最短路径
% 城市之间的距离矩阵为dist
dist = [0, 3, 2, 4, 5;
        3, 0, 6, 7, 8;
        2, 6, 0, 6, 7;
        4, 7, 6, 0, 3;
        5, 8, 7, 3, 0];
n = size(dist, 1); % 城市数量
num_iter = 100; % 迭代次数
pop_size = 50; % 种群大小
pc = 0.8; % 交叉概率
pm = 0.01; % 变异概率
pop = zeros(pop_size, n); % 种群
for i = 1:pop_size
    pop(i, :) = randperm(n); % 随机生成初始种群
end
best_dist = inf; % 最优距离
best_path = zeros(1, n); % 最优路径
for iter = 1:num_iter
    % 计算适应度
    dists = zeros(1, pop_size);
    for i = 1:pop_size
        path = pop(i, :);
        dists(i) = dist(path(n), path(1)); % 回到起点的距离
        for j = 1:n-1
            dists(i) = dists(i) + dist(path(j), path(j+1)); % 计算路径距离
        end
        if dists(i) < best_dist % 更新最优解
            best_dist = dists(i);
            best_path = path;
        end
    end
    % 选择
    [dists, idx] = sort(dists);
    pop = pop(idx, :);
    pop_new = zeros(pop_size, n);
    for i = 1:pop_size
        % 交叉
        if rand() < pc
            j = randi([1, pop_size]);
            p1 = pop(i, :);
            p2 = pop(j, :);
            pos = randperm(n, 2);
            pos = sort(pos);
            c1 = p1(pos(1):pos(2));
            c2 = setdiff(p2, c1, 'stable');
            pop_new(i, :) = [c1, c2];
        else
            pop_new(i, :) = pop(i, :);
        end
        % 变异
        if rand() < pm
            pos = randperm(n, 2);
            pos = sort(pos);
            pop_new(i, pos(1):pos(2)) = fliplr(pop_new(i, pos(1):pos(2)));
        end
    end
    pop = pop_new;
end
% 输出结果
fprintf('最短路径为：');
for i = 1:n-1
    fprintf('%d -> ', best_path(i));
end
fprintf('%d\n', best_path(n));
fprintf('路径长度为：%f\n', best_dist);

运行上述代码，即可得到最短路径和路径长度。