蚁群算法matlab代码

这里是一个简单的蚁群算法的Matlab代码示例：

% 蚁群算法求解TSP问题
% 城市坐标
city = [0.4000    0.4439
        0.2439    0.1463
        0.1707    0.2293
        0.2293    0.7610
        0.5171    0.9414
        0.8732    0.6537
        0.6878    0.5219
        0.8488    0.3609
        0.6683    0.2536
        0.6195    0.2634];
% 城市数量
numCities = size(city, 1);
% 生成初始蚂蚁位置
antPosition = zeros(numCities, 1);
for i = 1:numCities
    antPosition(i) = i;
end
% 设置参数
alpha = 1;
beta = 3;
q0 = 0.9;
rho = 0.1;
Q = 1;
maxIter = 100;
% 初始化信息素
pheromone = ones(numCities, numCities);
% 迭代寻优
for iter = 1:maxIter
    % 蚂蚁走路
    antPath = zeros(numCities, 1);
    antPath(1) = antPosition(1);
    for i = 2:numCities
        % 计算选择概率
        p = pheromone(antPosition(i-1), :) .^ alpha .* ((1 ./ city(:, 1)) .^ beta);
        p(antPath) = 0;
        p = p ./ sum(p);
        % 轮盘赌选择下一个城市
        if rand() < q0
            [~, index] = max(p);
        else
            index = rouletteWheelSelection(p);
        end
        antPath(i) = index;
    end
    % 计算路径长度
    len = 0;
    for i = 1:numCities-1
        len = len + norm(city(antPath(i), :) - city(antPath(i+1), :));
    end
    len = len + norm(city(antPath(numCities), :) - city(antPath(1), :));
    % 更新信息素
    deltaPheromone = zeros(numCities, numCities);
    for i = 1:numCities-1
        deltaPheromone(antPath(i), antPath(i+1)) = deltaPheromone(antPath(i), antPath(i+1)) + Q / len;
    end
    deltaPheromone(antPath(numCities), antPath(1)) = deltaPheromone(antPath(numCities), antPath(1)) + Q / len;
    pheromone = (1 - rho) .* pheromone + deltaPheromone;
end
% 打印最优路径
[~, index] = min(pheromone(:));
[row, col] = ind2sub(size(pheromone), index);
path = [row, col];
for i = 1:numCities-2
    [~, index] = max(pheromone(col, :));
    path = [path, index];
    col = index;
end
disp(path);

这份代码实现了一个简单的蚁群算法来求解TSP问题。其中，城市坐标存储在一个二维数组`city`中，`alpha`和`beta`分别为信息素和城市距离的权重，`q0`为选择最优路径的概率，`rho`为信息素挥发速率，`Q`为信息素增量系数，`maxIter`为最大迭代次数。在迭代过程中，程序会随机生成初始蚂蚁位置，然后进行蚂蚁走路，并通过更新信息素来优化路径。最终，程序会输出最优路径。