c语言实现蚁群算法代码

蚁群算法是一种模拟生物群体智能行为的优化算法，它的主要思想是模拟蚂蚁在寻找食物时的行为，通过多个蚂蚁的合作寻找全局最优解。下面是一个简单的C语言实现蚁群算法的代码示例：

#include <stdio.h>
#include <stdlib.h>
#include <time.h>

#define MAX_NODE 100 //最大节点数
#define MAX_ANT 100 //最大蚂蚁数

double pheromone[MAX_NODE][MAX_NODE]; //信息素矩阵
double delta[MAX_NODE][MAX_NODE]; //信息素增量矩阵
double distance[MAX_NODE][MAX_NODE]; //距离矩阵

int ant_pos[MAX_ANT]; //蚂蚁当前所在节点
int ant_path[MAX_ANT][MAX_NODE]; //蚂蚁已经走过的路径
double ant_distance[MAX_ANT]; //蚂蚁已经走过的路径长度

int node_num; //节点数
int ant_num; //蚂蚁数
int max_iter; //最大迭代次数

double alpha = 1.0; //信息素重要程度因子
double beta = 2.0; //启发函数重要程度因子
double rho = 0.5; //信息素挥发因子
double Q = 100.0; //信息素常量

void init()
{
    //初始化信息素矩阵和距离矩阵
    srand((unsigned)time(NULL));
    for (int i = 0; i < node_num; i++) {
        for (int j = i + 1; j < node_num; j++) {
            double d = (double)(rand() % 100) + 1.0;
            distance[i][j] = distance[j][i] = d;
            pheromone[i][j] = pheromone[j][i] = 1.0 / d;
            delta[i][j] = delta[j][i] = 0.0;
        }
    }
}

int select_next(int ant)
{
    //选择下一个节点
    double p[MAX_NODE];
    double sum = 0.0;
    int current_node = ant_pos[ant];
    for (int i = 0; i < node_num; i++) {
        if (i == current_node || ant_path[ant][i]) {
            p[i] = 0.0;
        } else {
            p[i] = pow(pheromone[current_node][i], alpha) * pow(1.0 / distance[current_node][i], beta);
            sum += p[i];
        }
    }
    double rand_value = (double)rand() / RAND_MAX;
    double roulette_wheel_position = rand_value * sum;
    double accumulate_probability = 0.0;
    for (int i = 0; i < node_num; i++) {
        if (i == current_node || ant_path[ant][i]) {
            continue;
        }
        accumulate_probability += p[i];
        if (accumulate_probability >= roulette_wheel_position) {
            return i;
        }
    }
    for (int i = 0; i < node_num; i++) {
        if (i == current_node || ant_path[ant][i]) {
            continue;
        }
        return i;
    }
}

void move_ant()
{
    //移动蚂蚁
    for (int i = 0; i < ant_num; i++) {
        int next_node = select_next(i);
        ant_pos[i] = next_node;
        ant_distance[i] += distance[ant_pos[i - 1]][next_node];
        ant_path[i][next_node] = 1;
    }
}

void update_pheromone()
{
    //更新信息素矩阵
    for (int i = 0; i < node_num; i++) {
        for (int j = i + 1; j < node_num; j++) {
            pheromone[i][j] = pheromone[j][i] = (1 - rho) * pheromone[i][j] + delta[i][j];
            delta[i][j] = delta[j][i] = 0.0;
        }
    }
}

void run()
{
    init();
    for (int iter = 0; iter < max_iter; iter++) {
        for (int i = 0; i < ant_num; i++) {
            ant_pos[i] = rand() % node_num;
            ant_distance[i] = 0.0;
            for (int j = 0; j < node_num; j++) {
                ant_path[i][j] = 0;
            }
            ant_path[i][ant_pos[i]] = 1;
        }
        for (int step = 1; step < node_num; step++) {
            move_ant();
        }
        for (int i = 0; i < ant_num; i++) {
            ant_distance[i] += distance[ant_pos[i]][ant_path[i]];
        }
        double best_distance = ant_distance;
        int best_ant_index = 0;
        for (int i = 1; i < ant_num; i++) {
            if (ant_distance[i] < best_distance) {
                best_distance = ant_distance[i];
                best_ant_index = i;
            }
        }
        for (int i = 0; i < node_num - 1; i++) {
            int start_node_index = ant_path[best_ant_index][i];
            int end_node_index = ant_path[best_ant_index][i + 1];
            delta[start_node_index][end_node_index] += Q / best_distance;
            delta[end_node_index][start_node_index] += Q / best_distance;
        }
        update_pheromone();
    }
}

int main()
{
    node_num = 10;
    ant_num = 20;
    max_iter = 50;
    run();
    return 0;
}