蝙蝠算法优化代码python

蝙蝠算法是一种基于蝙蝠行为的优化算法，用于求解优化问题。下面是一个使用Python实现蝙蝠算法优化的示例代码：

import numpy as np

# 定义蝙蝠类
class Bat:
    def __init__(self, position, velocity, frequency, pulse_rate):
        self.position = position  # 蝙蝠的位置
        self.velocity = velocity  # 蝙蝠的速度
        self.frequency = frequency  # 蝙蝠的频率
        self.pulse_rate = pulse_rate  # 蝙蝠的脉冲率

# 初始化蝙蝠种群
def init_bats(num_bats, num_dimensions, lower_bound, upper_bound):
    bats = []
    for _ in range(num_bats):
        position = np.random.uniform(lower_bound, upper_bound, num_dimensions)
        velocity = np.zeros(num_dimensions)
        frequency = np.random.uniform(0, 1)
        pulse_rate = np.random.uniform(0, 1)
        bats.append(Bat(position, velocity, frequency, pulse_rate))
    return bats

# 更新蝙蝠的位置和速度
def update_bats(bats, best_solution, alpha, gamma, lower_bound, upper_bound):
    for bat in bats:
        # 更新速度
        bat.velocity += (bat.position - best_solution) * bat.frequency
        # 更新位置
        bat.position += bat.velocity
        # 边界处理
        bat.position = np.clip(bat.position, lower_bound, upper_bound)
        # 脉冲发射
        if np.random.uniform(0, 1) > bat.pulse_rate:
            bat.position = best_solution + alpha * np.random.uniform(-1, 1)
        # 随机扰动
        bat.position += gamma * np.random.randn(len(bat.position))

# 计算适应度函数
def fitness_function(x):
    # 这里可以根据具体的优化问题定义适应度函数
    return np.sum(x ** 2)

# 蝙蝠算法优化
def bat_algorithm(num_bats, num_dimensions, num_iterations, lower_bound, upper_bound):
    bats = init_bats(num_bats, num_dimensions, lower_bound, upper_bound)
    best_solution = None
    best_fitness = float('inf')
    for _ in range(num_iterations):
        for bat in bats:
            fitness = fitness_function(bat.position)
            if fitness < best_fitness:
                best_solution = bat.position
                best_fitness = fitness
        update_bats(bats, best_solution, alpha=0.5, gamma=0.5, lower_bound=lower_bound, upper_bound=upper_bound)
    return best_solution

# 示例使用
num_bats = 10
num_dimensions = 2
num_iterations = 100
lower_bound = -5
upper_bound = 5

best_solution = bat_algorithm(num_bats, num_dimensions, num_iterations, lower_bound, upper_bound)
print("Best solution:", best_solution)