蚁群算法 旅行商 完整js代码

抱歉，根据提供的引用内容，没有找到完整的JavaScript代码实现蚁群算法解决旅行商问题的示例。但是，我可以为您提供一个基本的蚁群算法的伪代码示例，以帮助您理解算法的工作原理。

// 初始化参数
const numAnts = 10; // 蚂蚁数量
const numIterations = 100; // 迭代次数
const alpha = 1; // 信息素重要程度因子
const beta = 2; // 启发式信息重要程度因子
const rho = 0.5; // 信息素蒸发系数
const Q = 100; // 信息素增加强度

// 初始化城市距离矩阵和信息素矩阵
const distanceMatrix = [
  [0, 10, 15, 20],
  [10, 0, 35, 25],
  [15, 35, 0, 30],
  [20, 25, 30, 0]
];
const pheromoneMatrix = [
  [1, 1, 1, 1],
  [1, 1, 1, 1],
  [1, 1, 1, 1],
  [1, 1, 1, 1]
];

// 初始化最佳路径和最短距离
let bestPath = [];
let shortestDistance = Infinity;

// 迭代搜索
for (let iteration = 0; iteration < numIterations; iteration++) {
  // 每只蚂蚁进行路径选择
  for (let ant = 0; ant < numAnts; ant++) {
    const visited = new Set(); // 已访问的城市
    const path = []; // 当前蚂蚁的路径
    let currentCity = 0; // 当前所在城市

    // 选择下一个城市，直到所有城市都被访问
    while (visited.size < distanceMatrix.length) {
      visited.add(currentCity);
      path.push(currentCity);

      // 计算下一个城市的选择概率
      const probabilities = [];
      for (let city = 0; city < distanceMatrix.length; city++) {
        if (!visited.has(city)) {
          const pheromone = pheromoneMatrix[currentCity][city];
          const distance = distanceMatrix[currentCity][city];
          const probability = Math.pow(pheromone, alpha) * Math.pow(1 / distance, beta);
          probabilities.push({ city, probability });
        }
      }

      // 根据选择概率选择下一个城市
      const sumProbabilities = probabilities.reduce((sum, { probability }) => sum + probability, 0);
      const random = Math.random() * sumProbabilities;
      let cumulativeProbability = 0;
      for (const { city, probability } of probabilities) {
        cumulativeProbability += probability;
        if (random <= cumulativeProbability) {
          currentCity = city;
          break;
        }
      }
    }

    // 计算当前路径的总距离
    const distance = path.reduce((sum, city, index) => {
      const nextCity = path[(index + 1) % path.length];
      return sum + distanceMatrix[city][nextCity];
    }, 0);

    // 更新最佳路径和最短距离
    if (distance < shortestDistance) {
      bestPath = path;
      shortestDistance = distance;
    }
  }

  // 更新信息素矩阵
  for (let i = 0; i < pheromoneMatrix.length; i++) {
    for (let j = 0; j < pheromoneMatrix[i].length; j++) {
      pheromoneMatrix[i][j] *= (1 - rho); // 信息素蒸发
    }
  }
  for (let i = 0; i < bestPath.length; i++) {
    const city = bestPath[i];
    const nextCity = bestPath[(i + 1) % bestPath.length];
    pheromoneMatrix[city][nextCity] += Q / shortestDistance; // 信息素增加
  }
}

// 输出最佳路径和最短距离
console.log("Best path:", bestPath);
console.log("Shortest distance:", shortestDistance);