这个Matlab元码求解析！🙏🏻

% 蚁群算法求解TSP问题
% 参考资料:
% https://blog.csdn.net/weixin_45470127/article/details/105469546

% 问题描述:
% TSP问题，即旅行商问题，一个旅行商要经过多个城市，每个城市只能经过一次，求他所要走过的路径，使得总路程最短。

function [opt_route,opt_value] = ACO(points,N,m,iterations,alpha,beta,rho,Q)
% 参数说明:
% points: 城市坐标
% N: 城市数量
% m: 蚂蚁数量
% iterations: 迭代次数
% alpha: 表示信息素重要程度参数
% beta: 表示启发式因子重要程度参数
% rho: 表示信息素挥发系数
% Q: 表示信息素增加量

% 初始化参数
pheromone = ones(N,N)/N; % 信息素初始值
dist_matrix = zeros(N,N); % 距离矩阵
for i = 1:N
    for j = 1:N
        dist_matrix(i,j) = pdist([points(i,:);points(j,:)],'euclidean');
    end
end
eta = 1./dist_matrix; % 启发式因子

opt_route = zeros(iterations,N);
opt_value = inf;

% 开始迭代
for t = 1:iterations
    % 蚂蚁群并行搜索
    ant_route = zeros(m,N);
    for k = 1:m % 遍历每只蚂蚁
        available_city = [1:N]; % 初始化可用城市
        ant_route(k,1) = randi(N); % 随机选择起点
        available_city(ant_route(k,1)) = []; % 将起点从可用城市中移除
        for i = 2:N % 遍历每个城市
            p = rand; % 生成0-1之间的随机数
            if p < rand() % 以概率r选择下一步城市
                index = find(eta(ant_route(k,i-1),available_city) .* pheromone(ant_route(k,i-1),available_city).^alpha == max(eta(ant_route(k,i-1),available_city) .* pheromone(ant_route(k,i-1),available_city).^alpha));
                next_city = available_city(index(randi(length(index))));
            else % 否则选按照启发式信息选择下一步城市
                index = find(eta(ant_route(k,i-1),available_city).^beta == max(eta(ant_route(k,i-1),available_city).^beta));
                next_city = available_city(index(randi(length(index))));
            end
            available_city(next_city == available_city) = []; % 将已经经过的城市从可用城市中移除
            ant_route(k,i) = next_city;
        end
    end
    delta_pheromone = zeros(N,N); % 初始化信息素增加量矩阵
    distance = zeros(m,1);
    for k = 1:m % 计算每只蚂蚁的路径长度
        for i = 1:N-1
            distance(k) = distance(k) + dist_matrix(ant_route(k,i),ant_route(k,i+1));
        end
        distance(k) = distance(k) + dist_matrix(ant_route(k,N),ant_route(k,1));
    end
    [~,best_ant] = min(distance); % 找到最佳路径的蚂蚁
    if distance(best_ant) < opt_value % 更新最优路径
        opt_value = distance(best_ant);
        opt_route(t,:) = ant_route(best_ant,:);
    else
        opt_route(t,:) = opt_route(t-1,:);
    end
    for k = 1:m % 计算每只蚂蚁走过的路径上信息素增加的量
        for i = 1:N-1
            delta_pheromone(ant_route(k,i),ant_route(k,i+1)) = delta_pheromone(ant_route(k,i),ant_route(k,i+1)) + Q/distance(k);
            delta_pheromone(ant_route(k,i+1),ant_route(k,i)) = delta_pheromone(ant_route(k,i+1),ant_route(k,i)) + Q/distance(k);
        end
        delta_pheromone(ant_route(k,N),ant_route(k,1)) = delta_pheromone(ant_route(k,N),ant_route(k,1)) + Q/distance(k);
        delta_pheromone(ant_route(k,1),ant_route(k,N)) = delta_pheromone(ant_route(k,1),ant_route(k,N)) + Q/distance(k);
    end
    pheromone = (1-rho)*pheromone + delta_pheromone; % 更新信息素
    pheromone = min(pheromone,1); % 信息素上下限处理
    pheromone = max(pheromone,0.01);
end
opt_route = opt_route(iterations,:);
end