人工峰群算法 python

人工峰群算法（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。以下是使用Python实现ABC算法的示例代码：

import numpy as np

# 目标函数
def func(x):
    return x**2

# ABC算法
def abc_algorithm(func, dim, limit=100, colony_size=30, max_trials=100):
    # 初始化蜜蜂群
    colony = np.random.uniform(-limit, limit, (colony_size, dim))
    # 计算每个蜜蜂的适应度
    fitness = np.array([func(x) for x in colony])
    # 记录最优解和最优适应度
    best_solution = colony[np.argmin(fitness)]
    best_fitness = np.min(fitness)
    # 迭代搜索
    for i in range(max_trials):
        # 雇佣蜜蜂阶段
        for j in range(colony_size):
            # 随机选择一个不等于j的蜜蜂k
            k = np.random.choice([x for x in range(colony_size) if x != j])
            # 随机选择一个维度d
            d = np.random.randint(dim)
            # 生成新解
            new_solution = colony[j].copy()
            new_solution[d] = colony[j][d] + np.random.uniform(-1, 1) * (colony[j][d] - colony[k][d])
            # 计算新解的适应度
            new_fitness = func(new_solution)
            # 判断是否更新最优解和最优适应度
            if new_fitness < fitness[j]:
                colony[j] = new_solution
                fitness[j] = new_fitness
                if new_fitness < best_fitness:
                    best_solution = new_solution
                    best_fitness = new_fitness
        # 观察蜜蜂阶段
        for j in range(colony_size):
            # 随机选择一个维度d
            d = np.random.randint(dim)
            # 生成新解
            new_solution = colony[j].copy()
            new_solution[d] = np.random.uniform(-limit, limit)
            # 计算新解的适应度
            new_fitness = func(new_solution)
            # 判断是否更新最优解和最优适应度
            if new_fitness < fitness[j]:
                colony[j] = new_solution
                fitness[j] = new_fitness
                if new_fitness < best_fitness:
                    best_solution = new_solution
                    best_fitness = new_fitness
        # 跟随蜜蜂阶段
        for j in range(colony_size):
            # 随机选择一个不等于j的蜜蜂k
            k = np.random.choice([x for x in range(colony_size) if x != j])
            # 随机选择一个维度d
            d = np.random.randint(dim)
            # 生成新解
            new_solution = colony[j].copy()
            new_solution[d] = colony[j][d] + np.random.uniform(-1, 1) * (best_solution[d] - colony[j][d]) + np.random.uniform(-1, 1) * (colony[j][d] - colony[k][d])
            # 计算新解的适应度
            new_fitness = func(new_solution)
            # 判断是否更新最优解和最优适应度
            if new_fitness < fitness[j]:
                colony[j] = new_solution
                fitness[j] = new_fitness
                if new_fitness < best_fitness:
                    best_solution = new_solution
                    best_fitness = new_fitness
    return best_solution, best_fitness

# 示例：求解函数f(x)=x^2的最小值
best_solution, best_fitness = abc_algorithm(func, dim=1, limit=10)
print("最优解：", best_solution)
print("最优适应度：", best_fitness)