蚁群算法多目标点路径规划matlab代码

以下是一个简单的蚁群算法多目标点路径规划的Matlab代码示例：

% 假设有5个目标点，即需要经过的城市
num_cities = 5;

% 随机生成城市之间的距离矩阵
distances = rand(num_cities, num_cities);

% 初始化蚂蚁群
num_ants = 10;
pheromones = ones(num_cities, num_cities);
eta = 1 ./ distances;
Q = 1;
alpha = 1;
beta = 3;
evap_rate = 0.5;

% 开始迭代
num_iterations = 100;
for iteration = 1:num_iterations
    % 每只蚂蚁都要走一遍
    for ant = 1:num_ants
        % 随机选择一个起始城市
        current_city = randi(num_cities);
        unvisited_cities = 1:num_cities;
        unvisited_cities(current_city) = [];
        path = [current_city];
        % 开始循环遍历所有未访问的城市
        for i = 1:num_cities-1
            % 计算每个未访问城市的概率
            probabilities = pheromones(current_city, unvisited_cities).^alpha .* eta(current_city, unvisited_cities).^beta;
            probabilities = probabilities ./ sum(probabilities);
            % 根据概率选择下一个城市
            next_city_index = randsample(length(unvisited_cities), 1, true, probabilities);
            % 将下一个城市加入路径并更新当前城市
            current_city = unvisited_cities(next_city_index);
            unvisited_cities(next_city_index) = [];
            path = [path current_city];
        end
        % 计算当前路径的总距离
        total_distance = sum(distances(sub2ind(size(distances), path(1:end-1), path(2:end))));
        % 更新信息素矩阵
        delta_pheromones = zeros(num_cities, num_cities);
        for i = 1:num_cities-1
            delta_pheromones(path(i), path(i+1)) = Q / total_distance;
        end
        delta_pheromones(path(end), path(1)) = Q / total_distance;
        pheromones = (1-evap_rate) .* pheromones + delta_pheromones;
    end
end

% 找到最佳路径
best_path = [];
best_distance = Inf;
for ant = 1:num_ants
    current_city = randi(num_cities);
    unvisited_cities = 1:num_cities;
    unvisited_cities(current_city) = [];
    path = [current_city];
    for i = 1:num_cities-1
        probabilities = pheromones(current_city, unvisited_cities).^alpha .* eta(current_city, unvisited_cities).^beta;
        probabilities = probabilities ./ sum(probabilities);
        next_city_index = randsample(length(unvisited_cities), 1, true, probabilities);
        current_city = unvisited_cities(next_city_index);
        unvisited_cities(next_city_index) = [];
        path = [path current_city];
    end
    total_distance = sum(distances(sub2ind(size(distances), path(1:end-1), path(2:end))));
    if total_distance < best_distance
        best_path = path;
        best_distance = total_distance;
    end
end

% 输出结果
disp('最佳路径：');
disp(best_path);
disp('最短距离：');
disp(best_distance);

这个示例代码生成了一个随机的城市距离矩阵，然后使用蚁群算法找到经过所有城市的最短路径。你可以根据需要修改代码来适应你的具体问题。