ACS算法matlab代码

以下是ACS算法的Matlab代码：

function [bestSolution, bestFitness] = ACS(numAnts, numIterations, alpha, beta, rho, Q, distMatrix)
% numAnts: 蚂蚁数量
% numIterations: 迭代次数
% alpha: 信息素重要程度因子
% beta: 启发式因子
% rho: 信息素挥发因子
% Q: 常数因子
% distMatrix: 距离矩阵

% 初始化信息素矩阵
tau = ones(size(distMatrix));
% 初始化最优解和最优适应度
bestSolution = [];
bestFitness = Inf;

for iter = 1:numIterations
    % 初始化每个蚂蚁的当前位置
    currentCity = ones(numAnts, 1);
    % 初始化每个蚂蚁的已访问城市集合
    visitedCities = zeros(numAnts, size(distMatrix, 1));
    % 计算启发因子矩阵
    eta = 1./distMatrix;
    eta(eta == Inf) = 0;
    % 初始化每个蚂蚁的路径长度
    tourLength = zeros(numAnts, 1);
    
    % 每个蚂蚁依次选择下一个城市
    for cityCount = 2:size(distMatrix, 1)
        % 计算每个蚂蚁当前城市到其他未访问城市的转移概率
        probabilities = tau(currentCity,:).^alpha .* eta(currentCity,:).^beta;
        probabilities(:,visitedCities) = 0;
        % 对概率进行归一化
        probabilities = probabilities./sum(probabilities, 2);
        % 随机选择下一个城市
        nextCity = arrayfun(@(x) randsample(size(distMatrix, 1), 1, true, probabilities(x,:)), 1:numAnts)';
        % 更新当前城市和已访问城市集合
        visitedCities(sub2ind([numAnts, size(distMatrix, 1)], 1:numAnts, nextCity')) = 1;
        currentCity = nextCity;
        % 累计路径长度
        tourLength = tourLength + distMatrix(sub2ind(size(distMatrix), currentCity, nextCity));
    end
    
    % 计算每个蚂蚁的适应度
    fitness = tourLength;
    % 更新最优解和最优适应度
    [minFitness, minIndex] = min(fitness);
    if minFitness < bestFitness
        bestFitness = minFitness;
        bestSolution = currentCity(minIndex,:);
    end
    
    % 更新信息素矩阵
    deltaTau = zeros(size(distMatrix));
    for ant = 1:numAnts
        for cityCount = 1:(size(distMatrix, 1)-1)
            i = currentCity(ant);
            j = currentCity(ant+1);
            deltaTau(i,j) = deltaTau(i,j) + Q./tourLength(ant);
        end
    end
    tau = (1-rho).*tau + deltaTau;
end
end

其中，`numAnts` 为蚂蚁数量，`numIterations` 为迭代次数，`alpha` 为信息素重要程度因子，`beta` 为启发式因子，`rho` 为信息素挥发因子，`Q` 为常数因子，`distMatrix` 为距离矩阵。函数返回最优解和最优适应度。