帮我提供一个使用python写的鲸鱼优化算法

好的，以下是使用 Python 实现的鲸鱼优化算法（Whale Optimization Algorithm）的代码示例：

import numpy as np

# 定义鲸鱼优化算法类
class WOA:
    def __init__(self, obj_func, dim, search_range, population_size=50, max_iter=100):
        self.obj_func = obj_func  # 目标函数
        self.dim = dim  # 变量维数
        self.search_range = search_range  # 变量搜索范围
        self.population_size = population_size  # 种群大小
        self.max_iter = max_iter  # 最大迭代次数
        self.best_solution = None  # 最优解
        self.best_fitness = float("inf")  # 最优适应度
        self.population = np.zeros((self.population_size, self.dim))  # 种群位置
        self.fitness = np.zeros(self.population_size)  # 种群适应度

    # 初始化种群
    def init_population(self):
        for i in range(self.population_size):
            self.population[i] = np.random.uniform(self.search_range[0], self.search_range[1], self.dim)
        self.eval_fitness()

    # 计算适应度
    def eval_fitness(self):
        for i in range(self.population_size):
            self.fitness[i] = self.obj_func(self.population[i])
            if self.fitness[i] < self.best_fitness:
                self.best_solution = self.population[i]
                self.best_fitness = self.fitness[i]

    # 鲸鱼优化算法
    def optimize(self):
        self.init_population()
        for t in range(self.max_iter):
            a = 2 - t * (2 / self.max_iter)  # 衰减因子
            for i in range(self.population_size):
                r = np.random.uniform()  # 随机数
                A = 2 * a * r - a  # 修正系数A
                C = 2 * r  # 收缩系数C
                l = np.random.uniform(-1, 1, self.dim)  # 随机向量l
                p = np.random.uniform()  # 随机概率p
                if p < 0.5:
                    if np.abs(A) < 1:
                        D = np.abs(C * self.best_solution - self.population[i])
                        new_solution = self.best_solution - A * D * np.exp(1)**(0.5*l)  # 位置更新
                    else:
                        rand_whale = self.population[np.random.randint(0, self.population_size)]
                        D = np.abs(C * rand_whale - self.population[i])
                        new_solution = rand_whale - A * D * np.exp(1)**(0.5*l)  # 位置更新
                else:
                    distance = np.abs(self.best_solution - self.population[i])
                    new_solution = distance * np.exp(0.5*A*l) * np.cos(2*np.pi*A*l) + self.best_solution  # 位置更新
                new_solution = np.clip(new_solution, self.search_range[0], self.search_range[1])  # 位置约束
                new_fitness = self.obj_func(new_solution)  # 计算新位置的适应度
                if new_fitness < self.fitness[i]:
                    self.population[i] = new_solution  # 更新位置
                    self.fitness[i] = new_fitness  # 更新适应度
                    if new_fitness < self.best_fitness:
                        self.best_solution = new_solution  # 更新最优解
                        self.best_fitness = new_fitness  # 更新最优适应度
        return self.best_solution, self.best_fitness

在使用该鲸鱼优化算法类时，需要传入目标函数、变量维数、变量搜索范围、种群大小和最大迭代次数等参数，例如：

# 定义目标函数
def obj_func(x):
    return x[0]**2 + x[1]**2

# 初始化鲸鱼优化算法类
woa = WOA(obj_func, dim=2, search_range=[-5, 5], population_size=50, max_iter=100)

# 运行优化算法
best_solution, best_fitness = woa.optimize()

# 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)

上述示例代码中，目标函数为 $f(x_1, x_2) = x_1^2 + x_2^2$，变量维数为2，搜索范围为 [-5, 5]，种群大小为50，最大迭代次数为100。运行优化算法后输出最优解和最优适应度。