蝙蝠算法求解八维函数最大值python

以下是使用蝙蝠算法求解八维函数最大值的Python代码：

import random
import math

# 定义目标函数
def obj_func(x):
    return -(x[0]**2 + x[1]**2 + x[2]**2 + x[3]**2 + x[4]**2 + x[5]**2 + x[6]**2 + x[7]**2)

# 定义蝙蝠类
class Bat:
    def __init__(self, dim, lower_bound, upper_bound):
        self.dim = dim
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound
        self.position = [random.uniform(lower_bound, upper_bound) for i in range(dim)]
        self.velocity = [0 for i in range(dim)]
        self.frequency = 0
        self.pulse_rate = 0
        self.loudness = 0
        self.best_position = self.position[:]
        self.best_value = obj_func(self.position)

    # 更新频率、脉冲率和响度
    def update_params(self, best_bat, alpha, gamma):
        self.frequency = random.uniform(0, 1)
        self.pulse_rate = 1 - math.exp(-gamma * self.frequency)
        self.loudness = alpha ** self.frequency

        if random.uniform(0, 1) > self.pulse_rate:
            for i in range(self.dim):
                self.velocity[i] += (best_bat.position[i] - self.position[i]) * self.frequency
                self.position[i] += self.velocity[i]

        else:
            for i in range(self.dim):
                self.position[i] = best_bat.position[i] + random.uniform(-1, 1) * best_bat.loudness

        # 确保位置在边界内
        for i in range(self.dim):
            if self.position[i] < self.lower_bound:
                self.position[i] = self.lower_bound
            if self.position[i] > self.upper_bound:
                self.position[i] = self.upper_bound

        # 更新最佳位置和最佳值
        value = obj_func(self.position)
        if value > self.best_value:
            self.best_position = self.position[:]
            self.best_value = value

# 初始化参数
dim = 8  # 变量维度
pop_size = 20  # 种群大小
max_iter = 100  # 最大迭代次数
alpha = 0.9  # 响度衰减系数
gamma = 0.9  # 脉冲率衰减系数
lower_bound = -10  # 变量下界
upper_bound = 10  # 变量上界

# 初始化种群
population = [Bat(dim, lower_bound, upper_bound) for i in range(pop_size)]

# 迭代优化
for t in range(max_iter):
    # 更新每个蝙蝠的参数和位置
    for bat in population:
        best_bat = max(population, key=lambda x: x.best_value)
        bat.update_params(best_bat, alpha, gamma)

    # 输出当前最优解
    best_bat = max(population, key=lambda x: x.best_value)
    print("Iteration {}: Best Value = {:.6f}".format(t+1, -best_bat.best_value))

# 输出最终结果
best_bat = max(population, key=lambda x: x.best_value)
print("Best Solution: ", best_bat.best_position)
print("Best Value: ", -best_bat.best_value)

该代码使用了Python的面向对象编程思想，将蝙蝠定义为一个类，包含了蝙蝠的各种属性和方法。在主程序中，首先初始化种群，然后进行迭代优化，每次迭代更新每个蝙蝠的参数和位置，并输出当前最优解。最后输出最终结果，包括最优解和最优值。