人工蜂群算法C语言代码

以下是一个简单的人工蜂群算法的C语言代码示例：

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

#define MAX_TRIAL 100  // 最大尝试次数
#define MAX_EMPLOYEE 50  // 最大雇佣蜂数量
#define MAX_ONLOOKER 50  // 最大观察蜂数量
#define N 10  // 解空间维度
#define FOOD_NUMBER 50  // 食物源数量

// 目标函数
double objective_function(double x[N]) {
    double sum = 0.0;
    for (int i = 0; i < N; i++) {
        sum += pow(x[i], 2);
    }
    return sum;
}

// 随机生成解
void generate_solution(double x[N]) {
    for (int i = 0; i < N; i++) {
        x[i] = (double)rand() / RAND_MAX * 10 - 5;
    }
}

// 初始化蜜蜂群体
void init_population(double solutions[FOOD_NUMBER][N], double fitness[FOOD_NUMBER]) {
    for (int i = 0; i < FOOD_NUMBER; i++) {
        generate_solution(solutions[i]);
        fitness[i] = objective_function(solutions[i]);
    }
}

// 更新蜜蜂位置
void update_solution(double current_solution[N], double new_solution[N], double phi) {
    for (int i = 0; i < N; i++) {
        double r = (double)rand() / RAND_MAX;
        new_solution[i] = current_solution[i] + (r - 0.5) * phi;
    }
}

// 更新蜜蜂适应度
double update_fitness(double current_fitness, double new_solution[N]) {
    double new_fitness = objective_function(new_solution);
    return new_fitness < current_fitness ? new_fitness : current_fitness;
}

// 寻找最优解
void search_best_solution(double solutions[FOOD_NUMBER][N], double fitness[FOOD_NUMBER], double best_solution[N], double *best_fitness) {
    int best_index = 0;
    *best_fitness = fitness[0];
    for (int i = 1; i < FOOD_NUMBER; i++) {
        if (fitness[i] < *best_fitness) {
            best_index = i;
            *best_fitness = fitness[i];
        }
    }
    for (int i = 0; i < N; i++) {
        best_solution[i] = solutions[best_index][i];
    }
}

// 人工蜂群算法
void artificial_bee_colony_algorithm() {
    double solutions[FOOD_NUMBER][N];  // 食物源
    double fitness[FOOD_NUMBER];  // 适应度
    double best_solution[N];  // 最优解
    double best_fitness;  // 最优适应度

    init_population(solutions, fitness);
    search_best_solution(solutions, fitness, best_solution, &best_fitness);

    int trial = 0;
    while (trial < MAX_TRIAL) {
        // 雇佣蜂阶段
        for (int i = 0; i < MAX_EMPLOYEE; i++) {
            int index = rand() % FOOD_NUMBER;
            double current_solution[N];
            for (int j = 0; j < N; j++) {
                current_solution[j] = solutions[index][j];
            }
            double new_solution[N];
            update_solution(current_solution, new_solution, 1.0);
            double new_fitness = update_fitness(fitness[index], new_solution);
            if (new_fitness < fitness[index]) {
                for (int j = 0; j < N; j++) {
                    solutions[index][j] = new_solution[j];
                }
                fitness[index] = new_fitness;
            }
        }

        // 观察蜂阶段
        double sum_fitness = 0.0;
        for (int i = 0; i < FOOD_NUMBER; i++) {
            sum_fitness += 1.0 / fitness[i];
        }
        for (int i = 0; i < MAX_ONLOOKER; i++) {
            double r = (double)rand() / RAND_MAX;
            double select_prob = 0.0;
            int select_index = 0;
            for (int j = 0; j < FOOD_NUMBER; j++) {
                select_prob += 1.0 / fitness[j] / sum_fitness;
                if (r <= select_prob) {
                    select_index = j;
                    break;
                }
            }
            double current_solution[N];
            for (int j = 0; j < N; j++) {
                current_solution[j] = solutions[select_index][j];
            }
            double new_solution[N];
            update_solution(current_solution, new_solution, 1.0);
            double new_fitness = update_fitness(fitness[select_index], new_solution);
            if (new_fitness < fitness[select_index]) {
                for (int j = 0; j < N; j++) {
                    solutions[select_index][j] = new_solution[j];
                }
                fitness[select_index] = new_fitness;
            }
        }

        // 跟随蜂阶段
        search_best_solution(solutions, fitness, best_solution, &best_fitness);

        trial++;
    }

    printf("最优解：");
    for (int i = 0; i < N; i++) {
        printf("%lf ", best_solution[i]);
    }
    printf("\n最优适应度：%lf\n", best_fitness);
}

int main() {
    srand(time(NULL));
    artificial_bee_colony_algorithm();
    return 0;
}

这段代码实现了人工蜂群算法的基本框架，包括初始化蜜蜂群体、更新蜜蜂位置和适应度、寻找最优解等功能。其中目标函数为简单的凸函数，可根据实际问题进行修改。你可以根据自己的需求进行调整和优化