编写matlab蚁群算法求解tsp问题的代码，要求7个城市

以下是使用蚁群算法求解TSP问题的Matlab代码，假设有7个城市：

%% 初始化数据
num_city = 7; % 城市数量
num_ant = 50; % 蚂蚁数量
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 = 1; % 信息素更新常数
distance = [0 2 7 3 6 5 4;% 城市距离矩阵
            2 0 4 5 8 9 3;
            7 4 0 6 5 3 2;
            3 5 6 0 9 4 8;
            6 8 5 9 0 3 7;
            5 9 3 4 3 0 5;
            4 3 2 8 7 5 0];
pheromone = ones(num_city,num_city); % 信息素矩阵
best_distance = Inf; % 最佳路径距离
best_path = zeros(1,num_city); % 最佳路径

%% 蚁群算法迭代
for iter = 1:100 % 迭代次数
    % 初始化蚂蚁位置和已访问城市
    ant_pos = ones(num_ant,1);
    visited_city = zeros(num_ant,num_city);
    for ant = 1:num_ant
        visited_city(ant,1) = 1;
    end
    
    % 蚂蚁行走
    for step = 2:num_city
        for ant = 1:num_ant
            visited = visited_city(ant,:);
            unvisited = find(visited==0);
            prob = zeros(1,length(unvisited));
            for i = 1:length(unvisited)
                city = unvisited(i);
                prob(i) = pheromone(ant_pos(ant),city)^alpha * (1/distance(ant_pos(ant),city))^beta;
            end
            prob = prob/sum(prob);
            cum_prob = cumsum(prob);
            r = rand;
            next_city = unvisited(find(cum_prob>=r,1));
            visited_city(ant,step) = next_city;
            ant_pos(ant) = next_city;
        end
    end
    
    % 计算路径长度和更新最佳路径
    for ant = 1:num_ant
        path = visited_city(ant,:);
        dist = 0;
        for i = 1:num_city-1
            dist = dist + distance(path(i),path(i+1));
        end
        dist = dist + distance(path(num_city),path(1));
        if dist < best_distance
            best_distance = dist;
            best_path = path;
        end
        % 更新信息素
        for i = 1:num_city-1
            pheromone(path(i),path(i+1)) = (1-rho) * pheromone(path(i),path(i+1)) + Q/dist;
        end
        pheromone(path(num_city),path(1)) = (1-rho) * pheromone(path(num_city),path(1)) + Q/dist;
    end
end

%% 输出最佳路径和距离
disp('最佳路径：');
disp(best_path);
disp(['最佳路径距离： ' num2str(best_distance)]);