用python写鲸鱼优化算法

如果您要写一个鲸鱼优化算法，那么您可以使用 Python 进行实现。

鲸鱼优化算法是一种基于自适应优化的全局优化算法，它是借鉴了鲸鱼的漂浮行为来设计的。鲸鱼优化算法主要用于优化那些具有非线性、多峰、高维等特点的函数。

您可以利用 Python 中的相关科学计算库，如 NumPy、SciPy 等，来帮助您实现鲸鱼优化算法。

以下是一个简单的鲸鱼优化算法的代码实现：

import numpy as np
import random

def whale_optimization_algorithm(objective_function, bounds, population_size=50, generations=1000):
    def generate_random_solution(bounds):
        return [random.uniform(bounds[i][0], bounds[i][1]) for i in range(len(bounds))]
    
    def compute_objective_value(solution):
        return objective_function(*solution)
    
    def compute_a_and_c(A, C, a, c, D_i, X_i, X_j):
        for i in range(len(X_i)):
            r = np.random.uniform(-1, 1)
            A[i] = 2*a*r - a
            C[i] = 2*r
        
        distance = np.linalg.norm(X_i - X_j)
        p = np.random.uniform(0, 1)
        if p < 0.5:
            if abs(A) < 1:
                D_i = abs(C*distance)
            else:
                D_i = -1*np.log(p)*distance
        else:
            D_i = 1
        
        return A, C, D_i
    
    a = 2
    c = 1
    population = [generate_random_solution(bounds) for _ in range(population_size)]
    objective_values = [compute_objective_value(solution) for solution in population]
    for generation in range(generations):
        for i in range(population_size):
            A = np.zeros(population_size)
            C = np.zeros(population_size)
            D