二维路径规划蚁遗传算法matlab代码

以下是一个简单的二维路径规划蚁遗传算法的 Matlab 代码示例：

% 参数设置
num_ants = 50; % 蚂蚁数量
num_cities = 20; % 城市数量
alpha = 1; % 信息素重要程度因子
beta = 5; % 启发式因子
rho = 0.5; % 信息素挥发因子
Q = 100; % 每次信息素更新的常数
max_iter = 200; % 最大迭代次数

% 初始化城市距离和信息素矩阵
dist_matrix = rand(num_cities) * 10; % 随机生成距离矩阵
pheromone_matrix = ones(num_cities) / num_cities; % 初始化信息素矩阵

% 初始化蚂蚁位置和路径
ant_position = randi(num_cities, num_ants, 1); % 随机生成蚂蚁位置
ant_path = zeros(num_ants, num_cities); % 初始化蚂蚁路径
ant_path(:, 1) = ant_position; % 将起点加入路径中

% 开始迭代
for iter = 1:max_iter
    % 计算每只蚂蚁的路径
    for i = 1:num_ants
        % 初始化已访问城市列表和未访问城市列表
        visited_cities = ant_position(i);
        unvisited_cities = setdiff(1:num_cities, visited_cities);
        
        % 选择下一个城市
        for j = 2:num_cities
            probs = pheromone_matrix(visited_cities, unvisited_cities).^alpha .* (1./dist_matrix(visited_cities, unvisited_cities)).^beta;
            probs = probs / sum(probs);
            next_city = randsample(unvisited_cities, 1, true, probs);
            ant_position(i) = next_city;
            visited_cities = [visited_cities, next_city];
            unvisited_cities = setdiff(unvisited_cities, next_city);
        end
        
        % 更新蚂蚁路径
        ant_path(i, :) = visited_cities;
    end
    
    % 计算每条路径的长度和适应度
    path_lengths = zeros(num_ants, 1);
    for i = 1:num_ants
        path_lengths(i) = sum(dist_matrix(sub2ind([num_cities, num_cities], ant_path(i, 1:end-1), ant_path(i, 2:end))));
    end
    fitness = 1./path_lengths;
    
    % 更新信息素矩阵
    delta_pheromone = zeros(num_cities);
    for i = 1:num_ants
        for j = 1:num_cities-1
            delta_pheromone(ant_path(i, j), ant_path(i, j+1)) = delta_pheromone(ant_path(i, j), ant_path(i, j+1)) + Q/fitness(i);
        end
        delta_pheromone(ant_path(i, end), ant_path(i, 1)) = delta_pheromone(ant_path(i, end), ant_path(i, 1)) + Q/fitness(i);
    end
    pheromone_matrix = (1-rho)*pheromone_matrix + delta_pheromone;
end

% 输出结果
best_path = ant_path(find(path_lengths == min(path_lengths), 1), :);
fprintf('最短路径：');
fprintf('%d ', best_path);
fprintf('\n路径长度：%f\n', min(path_lengths));

这个代码示例中，我们随机生成了一个二十个城市的地图，并利用蚂蚁遗传算法来寻找最短路径。在算法的每一次迭代中，我们先利用当前信息素矩阵和启发式函数来计算每只蚂蚁的下一步移动，然后计算蚂蚁路径的长度和适应度，并更新信息素矩阵。最终，我们输出了找到的最短路径和路径长度。