人工蜂群算法python

人工蜂群算法（Artificial Bee Colony Algorithm，简称ABC算法）是一种新兴的智能优化算法，它的灵感来源于蜜蜂采蜜的行为。以下是一个使用Python实现ABC算法的示例代码：

import random
import math

# 目标函数
def func(x):
    return x*x

# ABC算法
def abc_algorithm(dimension, search_range, max_iter, limit):
    # 初始化蜜蜂个体
    pop_size = limit * dimension
    pop_pos = []
    pop_val = []
    for i in range(pop_size):
        pos = []
        for j in range(dimension):
            pos.append(random.uniform(search_range[j][0], search_range[j][1]))
        val = func(pos)
        pop_pos.append(pos)
        pop_val.append(val)

    # 记录最优解
    best_val = min(pop_val)
    best_pos = pop_pos[pop_val.index(best_val)]

    # 迭代搜索
    for iter in range(max_iter):
        # employed bees phase
        for i in range(pop_size):
            # 随机选取一个不等于i的蜜蜂
            k = i
            while k == i:
                k = random.randint(0, pop_size-1)

            # 随机选择一个维度
            j = random.randint(0, dimension-1)

            # 生成新解
            phi = random.uniform(-1, 1)
            new_pos = pop_pos[i].copy()
            new_pos[j] = pop_pos[i][j] + phi * (pop_pos[i][j] - pop_pos[k][j])
            new_pos[j] = max(new_pos[j], search_range[j][0])
            new_pos[j] = min(new_pos[j], search_range[j][1])
            new_val = func(new_pos)

            # 更新解
            if new_val < pop_val[i]:
                pop_pos[i] = new_pos
                pop_val[i] = new_val
            else:
                limit -= 1

            # 更新最优解
            if new_val < best_val:
                best_val = new_val
                best_pos = new_pos

            # 判断是否达到搜索次数上限
            if limit == 0:
                return best_val, best_pos

        # onlooker bees phase
        fitness_sum = sum([1 / v for v in pop_val])
        prob = [1 / (v * fitness_sum) for v in pop_val]
        selected = []
        for i in range(pop_size):
            r = random.uniform(0, 1)
            accu = 0
            for j, p in enumerate(prob):
                accu += p
                if r <= accu:
                    selected.append(j)
                    break

        for i in selected:
            k = i
            while k == i:
                k = random.randint(0, pop_size-1)

            j = random.randint(0, dimension-1)

            phi = random.uniform(-1, 1)
            new_pos = pop_pos[i].copy()
            new_pos[j] = pop_pos[i][j] + phi * (pop_pos[i][j] - pop_pos[k][j])
            new_pos[j] = max(new_pos[j], search_range[j][0])
            new_pos[j] = min(new_pos[j], search_range[j][1])
            new_val = func(new_pos)

            if new_val < pop_val[i]:
                pop_pos[i] = new_pos
                pop_val[i] = new_val
            else:
                limit -= 1

            if new_val < best_val:
                best_val = new_val
                best_pos = new_pos

            if limit == 0:
                return best_val, best_pos

        # scout bees phase
        for i in range(pop_size):
            if limit == 0:
                return best_val, best_pos

            if pop_val[i] == max(pop_val):
                pos = []
                for j in range(dimension):
                    pos.append(random.uniform(search_range[j][0], search_range[j][1]))
                val = func(pos)
                pop_pos[i] = pos
                pop_val[i] = val
                limit -= 1

    return best_val, best_pos

# 测试
if __name__ == '__main__':
    # 定义搜索空间和参数
    dimension = 2
    search_range = [(-5, 5)] * dimension
    max_iter = 1000
    limit = 100

    # 运行ABC算法
    best_val, best_pos = abc_algorithm(dimension, search_range, max_iter, limit)

    # 输出结果
    print('best_val:', best_val)
    print('best_pos:', best_pos)

在上述代码中，`func`函数表示要优化的目标函数，`abc_algorithm`函数是ABC算法的实现。其中，`dimension`表示解向量维度，`search_range`表示搜索空间，`max_iter`表示最大迭代次数，`limit`表示每个蜜蜂最多搜索次数。在函数中，首先初始化蜜蜂个体，并记录最优解。然后进行迭代搜索，分别进行雇佣蜂阶段、观察蜂阶段和侦查蜂阶段。最终返回搜索结果。