固定出发点的蚁群算法代码MATLAB

下面是一个基本的固定出发点的蚁群算法的MATLAB代码示例：

% 参数设置
num_ants = 20;          % 蚂蚁数量
num_iterations = 100;   % 迭代次数
alpha = 1;              % 信息素重要程度
beta = 1;               % 启发式因子重要程度
rho = 0.5;              % 信息素蒸发率
Q = 1;                  % 信息素增强量

% 初始化城市距离矩阵和信息素矩阵
distances = [...];      % 城市之间的距离矩阵
pheromones = ones(size(distances));   % 信息素矩阵

% 迭代优化
best_path = zeros(1, num_iterations);
for iteration = 1:num_iterations
    % 初始化蚂蚁位置和路径
    current_city = randi(size(distances, 1));   % 随机选择起始城市
    path = current_city;                        % 记录蚂蚁路径
    
    % 蚂蚁移动
    for ant = 2:num_ants
        % 计算每个城市的选择概率
        unvisited_cities = setdiff(1:size(distances, 1), path);
        probabilities = zeros(1, length(unvisited_cities));
        for i = 1:length(unvisited_cities)
            city = unvisited_cities(i);
            probabilities(i) = (pheromones(current_city, city)^alpha) * (1/distances(current_city, city))^beta;
        end
        probabilities = probabilities / sum(probabilities);
        
        % 使用轮盘赌选择下一个城市
        next_city = roulette_wheel_selection(probabilities);
        
        % 更新路径和当前城市
        path = [path, next_city];
        current_city = next_city;
    end
    
    % 更新信息素
    delta_pheromones = zeros(size(distances));
    for ant = 1:num_ants
        for i = 1:length(path)-1
            city1 = path(i);
            city2 = path(i+1);
            delta_pheromones(city1, city2) = delta_pheromones(city1, city2) + Q / distances(city1, city2);
        end
    end
    pheromones = (1-rho) * pheromones + delta_pheromones;
    
    % 记录最佳路径
    best_path(iteration) = sum(distances(sub2ind(size(distances), path(1:end-1), path(2:end))));
end

% 打印结果
disp('Best path:');
disp(path);
disp('Total distance:');
disp(best_path(end));

% 轮盘赌选择函数
function selected_index = roulette_wheel_selection(probabilities)
    cumulative_probabilities = cumsum(probabilities);
    random_number = rand();
    selected_index = find(cumulative_probabilities >= random_number, 1);
end

请注意，上述代码中的 `distances` 矩阵应该根据实际问题进行定义，其中包含城市之间的距离信息。你需要根据你的问题自行填充 `distances` 矩阵。此外，代码中的参数也可以根据实际情况进行调整。最后，你可以通过 `best_path` 数组来查看每次迭代后的最佳路径长度。