人工蜂群算法 python

人工蜂群算法（Artificial Bee Colony Algorithm，ABC算法）是一种基于蜜蜂觅食行为的优化算法，常用于解决函数优化问题。以下是一个简单的 Python 实现：

import random
import math

# 目标函数
def func(x):
    return math.sin(x)

# ABC 算法
def abc(max_iter, n, limit):
    # 初始化种群
    population = []
    for i in range(n):
        population.append(random.uniform(-10, 10))
    # 迭代
    for t in range(max_iter):
        # 雇佣蜜蜂阶段
        for i in range(n):
            # 随机选择一个不等于 i 的蜜蜂
            k = random.randint(0, n-1)
            while k == i:
                k = random.randint(0, n-1)
            # 生成新解
            v = population[i] + random.uniform(-1, 1) * (population[i] - population[k])
            # 判断是否越界
            if v < -10:
                v = -10
            elif v > 10:
                v = 10
            # 计算适应度
            fv = func(v)
            fi = func(population[i])
            # 更新种群
            if fv < fi:
                population[i] = v
        # 观察蜜蜂阶段
        for i in range(n):
            # 生成新解
            v = random.uniform(-10, 10)
            # 判断是否越界
            if v < -10:
                v = -10
            elif v > 10:
                v = 10
            # 计算适应度
            fv = func(v)
            fi = func(population[i])
            # 更新种群
            if fv < fi:
                population[i] = v
        # 侦查蜜蜂阶段
        for i in range(n):
            # 生成新解
            v = random.uniform(-10, 10)
            # 判断是否越界
            if v < -10:
                v = -10
            elif v > 10:
                v = 10
            # 计算适应度
            fv = func(v)
            fi = func(population[i])
            # 更新种群
            if fv < fi:
                population[i] = v
        # 判断是否收敛
        best = min(population, key=func)
        if func(best) < limit:
            break
    return best

# 示例
best = abc(100, 50, 0.001)
print(best)