编写蚁群算法源代码

好的，以下是一个简单的蚁群算法的C++实现，供您参考：

#include <iostream>
#include <cstdlib>
#include <ctime>
#include <vector>

using namespace std;

// 定义常量
const int CITY_COUNT = 50;               // 城市数量
const int ANTS_COUNT = 100;              // 蚂蚁数量
const int MAX_GENERATION = 500;          // 最大迭代次数
const double ALPHA = 1.0;                // 信息素重要程度因子
const double BETA = 5.0;                 // 启发式因子
const double RHO = 0.5;                  // 信息素挥发速度
const double Q = 100.0;                  // 常系数
const double INF = 1e9;                  // 无穷大

// 城市坐标
double city_x[CITY_COUNT] = { 0 };
double city_y[CITY_COUNT] = { 0 };

// 距离矩阵
double distance[CITY_COUNT][CITY_COUNT] = { 0 };

// 信息素矩阵
double pheromone[CITY_COUNT][CITY_COUNT] = { 0 };

// 蚂蚁类
class Ant {
public:
    Ant() {
        tabu.push_back(rand() % CITY_COUNT);
        visited[tabu.back()] = true;
    }

    void clear() {
        distance_travelled = 0.0;
        visited.clear();
        for (int i = 0; i < CITY_COUNT; i++) {
            visited.push_back(false);
        }
        tabu.clear();
        tabu.push_back(rand() % CITY_COUNT);
        visited[tabu.back()] = true;
    }

    void move() {
        int current = tabu.back();
        int next = -1;
        double sum_prob = 0.0;
        vector<double> probs(CITY_COUNT, 0.0);

        // 计算概率
        for (int i = 0; i < CITY_COUNT; i++) {
            if (!visited[i]) {
                double prob = pow(pheromone[current][i], ALPHA) * pow(1.0 / distance[current][i], BETA);
                probs[i] = prob;
                sum_prob += prob;
            }
        }

        // 轮盘赌选择下一个城市
        double r = (double)rand() / RAND_MAX;
        double p = 0.0;
        for (int i = 0; i < CITY_COUNT; i++) {
            if (!visited[i]) {
                p += probs[i] / sum_prob;
                if (r <= p) {
                    next = i;
                    break;
                }
            }
        }

        // 如果没有下一个城市，则随机选择一个
        if (next == -1) {
            for (int i = 0; i < CITY_COUNT; i++) {
                if (!visited[i]) {
                    next = i;
                    break;
                }
            }
        }

        // 更新信息素
        pheromone[current][next] += Q / distance[current][next];
        pheromone[next][current] = pheromone[current][next];

        // 更新路径和禁忌表
        tabu.push_back(next);
        visited[next] = true;
        distance_travelled += distance[current][next];
    }

    double distance_travelled = 0.0;    // 路径长度
    vector<int> tabu;                   // 禁忌表
    vector<bool> visited;               // 是否访问过
};

// 初始化城市坐标和距离矩阵
void init() {
    srand(time(NULL));
    for (int i = 0; i < CITY_COUNT; i++) {
        city_x[i] = (double)rand() / RAND_MAX;
        city_y[i] = (double)rand() / RAND_MAX;
    }
    for (int i = 0; i < CITY_COUNT; i++) {
        for (int j = 0; j < CITY_COUNT; j++) {
            distance[i][j] = sqrt((city_x[i] - city_x[j]) * (city_x[i] - city_x[j]) + (city_y[i] - city_y[j]) * (city_y[i] - city_y[j]));
            pheromone[i][j] = 1.0;
        }
    }
}

// 蚂蚁算法主函数
void ant_algorithm() {
    vector<Ant> ants(ANTS_COUNT);
    Ant best_ant;
    double best_distance = INF;

    // 迭代
    for (int gen = 0; gen < MAX_GENERATION; gen++) {
        // 移动每只蚂蚁
        for (int i = 0; i < ANTS_COUNT; i++) {
            ants[i].clear();
            for (int j = 0; j < CITY_COUNT - 1; j++) {
                ants[i].move();
            }
            ants[i].distance_travelled += distance[ants[i].tabu.back()][ants[i].tabu.front()];
            if (ants[i].distance_travelled < best_distance) {
                best_distance = ants[i].distance_travelled;
                best_ant = ants[i];
            }
        }

        // 更新信息素
        for (int i = 0; i < CITY_COUNT; i++) {
            for (int j = 0; j < CITY_COUNT; j++) {
                pheromone[i][j] *= (1.0 - RHO);
            }
        }
        for (int i = 0; i < CITY_COUNT - 1; i++) {
            pheromone[best_ant.tabu[i]][best_ant.tabu[i + 1]] += Q / best_ant.distance_travelled;
            pheromone[best_ant.tabu[i + 1]][best_ant.tabu[i]] = pheromone[best_ant.tabu[i]][best_ant.tabu[i + 1]];
        }
        pheromone[best_ant.tabu.back()][best_ant.tabu.front()] += Q / best_ant.distance_travelled;
        pheromone[best_ant.tabu.front()][best_ant.tabu.back()] = pheromone[best_ant.tabu.back()][best_ant.tabu.front()];

        // 输出结果
        cout << "Generation " << gen << ": best distance = " << best_distance << endl;
    }
}

int main() {
    init();
    ant_algorithm();
    return 0;
}

这个代码实现了一个简单的蚁群算法，用于求解TSP问题。在代码中，我们定义了城市数量、蚂蚁数量、最大迭代次数、信息素重要程度因子、启发式因子、信息素挥发速度、常系数和无穷大等常量。然后初始化了城市坐标和距离矩阵，并定义了蚂蚁类和蚂蚁算法主函数。在算法主函数中，我们迭代了若干次，每次移动每只蚂蚁，并更新信息素。最后输出了最优解。