写一段matlab程序，利用蚁群算法求解高铁列车停站方案

以下是一个简单的matlab程序，利用蚁群算法求解高铁列车停站方案：

% 蚁群算法求解高铁列车停站方案

% 定义问题
stations = ["北京", "天津", "石家庄", "济南", "合肥", "南京", "上海"];
distances = [0, 117, 276, 376, 733, 944, 1318;
             117, 0, 236, 355, 612, 823, 1206;
             276, 236, 0, 274, 531, 742, 1115;
             376, 355, 274, 0, 257, 468, 851;
             733, 612, 531, 257, 0, 211, 584;
             944, 823, 742, 468, 211, 0, 373;
             1318, 1206, 1115, 851, 584, 373, 0]; % 距离矩阵

ants = 100; % 蚂蚁数量
alpha = 1; % 信息启发因子
beta = 2; % 期望启发因子
rho = 0.5; % 信息素挥发因子
Q = 1; % 常数因子
iterations = 50; % 迭代次数

% 初始化信息素矩阵
pheromones = ones(length(stations), length(stations));

% 迭代
for i = 1:iterations
    % 初始化每只蚂蚁
    for j = 1:ants
        current_station = randi(length(stations));
        visited_stations = [current_station];
        while length(visited_stations) < length(stations)
            % 计算下一个站点的概率
            probabilities = zeros(length(stations), 1);
            for k = 1:length(stations)
                if ~any(visited_stations == k)
                    probabilities(k) = pheromones(current_station, k)^alpha * (1 / distances(current_station, k))^beta;
                end
            end
            probabilities = probabilities / sum(probabilities);
            % 选择下一个站点
            r = rand;
            c = cumsum(probabilities);
            next_station = find(r <= c, 1);
            visited_stations = [visited_stations, next_station];
            current_station = next_station;
        end
        % 计算路线长度
        route_length = 0;
        for k = 1:length(stations)-1
            route_length = route_length + distances(visited_stations(k), visited_stations(k+1));
        end
        % 更新信息素矩阵
        for k = 1:length(stations)-1
            pheromones(visited_stations(k), visited_stations(k+1)) = (1 - rho) * pheromones(visited_stations(k), visited_stations(k+1)) + rho * Q / route_length;
            pheromones(visited_stations(k+1), visited_stations(k)) = (1 - rho) * pheromones(visited_stations(k+1), visited_stations(k)) + rho * Q / route_length;
        end
    end
end

% 找到最优路线
best_route = zeros(1, length(stations));
current_station = randi(length(stations));
visited_stations = [current_station];
while length(visited_stations) < length(stations)
    % 计算下一个站点的概率
    probabilities = zeros(length(stations), 1);
    for k = 1:length(stations)
        if ~any(visited_stations == k)
            probabilities(k) = pheromones(current_station, k)^alpha * (1 / distances(current_station, k))^beta;
        end
    end
    probabilities = probabilities / sum(probabilities);
    % 选择下一个站点
    [~, next_station] = max(probabilities);
    visited_stations = [visited_stations, next_station];
    best_route(length(visited_stations)) = next_station;
    current_station = next_station;
end

% 输出结果
disp("最优路线：");
for i = 1:length(best_route)
    disp(stations(best_route(i)));
end

该程序定义了问题、蚂蚁数量、启发因子、信息素挥发因子、常数因子和迭代次数等参数。然后初始化信息素矩阵，并在每次迭代中更新信息素矩阵。在每次迭代中，每只蚂蚁都会从一个随机站点出发，按照一定的概率选择下一个站点，直到经过所有站点。最后，程序找到最优路线，并输出结果。