用ACO-PSO解决三维TSP问题matlab代码

以下是使用MATLAB实现ACO-PSO算法解决三维TSP问题的示例代码。需要注意的是，此代码仅作为示例，实际应用中需要根据具体问题进行参数优化和算法调整。

% 三维TSP问题ACO-PSO算法示例

% 初始化参数
numAnts = 20; % 蚂蚁数量
numIterations = 100; % 迭代次数
alpha = 1; % 信息素权重因子
beta = 5; % 启发式信息权重因子
q0 = 0.9; % 信息素挥发因子
rho = 0.1; % 信息素残留因子
vmax = 5; % 粒子最大速度
c1 = 2; % 学习因子1
c2 = 2; % 学习因子2
w = 0.5; % 惯性权重
numParticles = 20; % 粒子数量

% 生成初始解
numCities = 10; % 城市数量
cities = rand(numCities, 3); % 生成城市坐标
distMatrix = pdist2(cities, cities); % 计算城市之间的距离
pheromoneMatrix = ones(numCities, numCities); % 初始化信息素矩阵

% 初始化蚂蚁的位置和速度
antPositions = rand(numAnts, 1) * numCities + 1; % 随机生成初始位置
antVelocities = zeros(numAnts, 1); % 初始速度为0

% 初始化粒子的位置和速度
particlePositions = rand(numParticles, numCities) * numCities + 1; % 随机生成初始位置
particleVelocities = zeros(numParticles, numCities); % 初始速度为0

% 开始迭代
for iteration = 1:numIterations
    % 更新蚂蚁的位置和速度
    for ant = 1:numAnts
        % 计算每个蚂蚁的下一个移动位置
        currentCity = antPositions(ant);
        unvisitedCities = setdiff(1:numCities, currentCity);
        pheromoneValues = pheromoneMatrix(currentCity, unvisitedCities);
        distanceValues = distMatrix(currentCity, unvisitedCities);
        heuristicValues = 1 ./ distanceValues;
        probabilities = (pheromoneValues .^ alpha) .* (heuristicValues .^ beta);
        probabilities = probabilities / sum(probabilities);
        if rand < q0
            [~, nextCity] = max(probabilities);
        else
            nextCity = randsample(unvisitedCities, 1, true, probabilities);
        end
        % 更新速度和位置
        deltaV = c1 * rand * (particlePositions(:, currentCity) - antPositions(ant)) + ...
            c2 * rand * (particlePositions(:, nextCity) - antPositions(ant));
        antVelocities(ant) = min(max(antVelocities(ant) + deltaV, -vmax), vmax);
        antPositions(ant) = antPositions(ant) + antVelocities(ant);
    end
    
    % 更新信息素
    deltaPheromoneMatrix = zeros(numCities, numCities);
    for ant = 1:numAnts
        tourLength = 0;
        for i = 1:numCities-1
            tourLength = tourLength + distMatrix(antPositions(ant), antPositions(ant + 1));
        end
        tourLength = tourLength + distMatrix(antPositions(ant), antPositions(1));
        for i = 1:numCities-1
            deltaPheromoneMatrix(antPositions(ant), antPositions(ant + 1)) = deltaPheromoneMatrix(antPositions(ant), antPositions(ant + 1)) + 1 / tourLength;
        end
        deltaPheromoneMatrix(antPositions(ant), antPositions(1)) = deltaPheromoneMatrix(antPositions(ant), antPositions(1)) + 1 / tourLength;
    end
    pheromoneMatrix = (1 - rho) * pheromoneMatrix + deltaPheromoneMatrix;
    
    % 更新粒子的位置和速度
    for particle = 1:numParticles
        % 计算每个粒子的下一个移动位置
        currentCity = find(particlePositions(particle,:) == max(particlePositions(particle,:)));
        unvisitedCities = setdiff(1:numCities, currentCity);
        pheromoneValues = pheromoneMatrix(currentCity, unvisitedCities);
        distanceValues = distMatrix(currentCity, unvisitedCities);
        heuristicValues = 1 ./ distanceValues;
        probabilities = (pheromoneValues .^ alpha) .* (heuristicValues .^ beta);
        probabilities = probabilities / sum(probabilities);
        if rand < q0
            [~, nextCity] = max(probabilities);
        else
            nextCity = randsample(unvisitedCities, 1, true, probabilities);
        end
        % 更新速度和位置
        deltaV = c1 * rand * (particlePositions(particle, currentCity) - particlePositions(particle, nextCity)) + ...
            c2 * rand * (particlePositions(particle, currentCity) - antPositions(1));
        particleVelocities(particle, currentCity) = min(max(particleVelocities(particle, currentCity) + deltaV, -vmax), vmax);
        particlePositions(particle, :) = particlePositions(particle, :) + particleVelocities(particle, :);
    end
    
    % 更新惯性权重
    w = w - (0.5 / numIterations);
end

% 输出最优解
bestTour = antPositions;
bestLength = 0;
for i = 1:numCities-1
    bestLength = bestLength + distMatrix(bestTour(i), bestTour(i + 1));
end
bestLength = bestLength + distMatrix(bestTour(numCities), bestTour(1));
fprintf('最优解为：%f\n', bestLength);