蜂群优化算法python代码

蜂群优化算法（Bee Colony Optimization，BCO）是一种启发式算法，模拟了蜜蜂在寻找蜜源和选择蜂巢位置的过程。在Python中可以使用以下代码实现蜂群优化算法：

import random
import numpy as np

# 定义目标函数
def fitness_function(x):
    return np.sum(np.square(x))

# 初始化蜜蜂种群
num_bees = 50
num_dimensions = 10
lower_bound = -10
upper_bound = 10
best_solution = None
best_fitness = float('inf')

# 随机生成初始解
population = [np.random.uniform(lower_bound, upper_bound, num_dimensions) for _ in range(num_bees)]

# 迭代次数
num_iterations = 1000

# 开始优化
for itr in range(num_iterations):
    for i in range(num_bees):
        new_solution = population[i] + np.random.uniform(-1, 1, num_dimensions)
        new_solution_fitness = fitness_function(new_solution)
        if new_solution_fitness < best_fitness:
            best_solution = new_solution
            best_fitness = new_solution_fitness
    new_population = []
    for i in range(num_bees):
        if random.random() < 0.2:
            new_population.append(np.random.uniform(lower_bound, upper_bound, num_dimensions))
        else:
            new_population.append(population[i])
    population = new_population

# 输出最优解和最优适应度
print("最优解：", best_solution)
print("最优适应度：", best_fitness)

以上代码首先定义了优化的目标函数fitness_function，然后初始化蜜蜂种群，随机生成初始解，并进行一定次数的迭代优化。在每一次迭代中，蜜蜂依据当前解向周围搜索新的解，然后根据一定概率更新种群状态。最终输出最优解和最优适应度。