鲸鱼优化算法 python

鲸鱼优化算法是一种基于模拟鲸鱼族群行为的进化优化算法，其核心思想是通过模拟鲸鱼族群中的个体行为（如浮游、潜水、跳跃、迁徙等）来寻找最优解。下面是一个简单的使用Python实现鲸鱼优化算法的例子：

import numpy as np

def function(x):
    # 计算目标函数值
    return x**2

def whale_optimization_algorithm(function, dim, search_space, max_iter):
    # 参数初始化
    population_size = 30
    a = 2
    b = 0.5
    l = 2 * np.random.rand(population_size, dim) - 1
    p = np.zeros((population_size, dim))
    fitness = np.zeros(population_size)
    for i in range(population_size):
        p[i] = search_space[0] + (search_space[1] - search_space[0]) * np.random.rand(dim)
        fitness[i] = function(p[i])

    # 开始迭代
    for t in range(max_iter):
        for i in range(population_size):
            # 浮游
            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A = a * l[i] - r1
            C = 2 * r2

            # 跳跃
            p_best = p[fitness.argmin()]
            D = np.abs(C * p_best - p[i])
            new_p = p_best - A * D

            # 更新鲸鱼位置
            new_p = np.clip(new_p, search_space[0], search_space[1])
            new_fitness = function(new_p)
            if new_fitness < fitness[i]:
                p[i] = new_p
                fitness[i] = new_fitness

        # 迁徙
        alpha = 2 - t * 2 / max_iter
        for i in range(population_size):
            r3 = np.random.rand(dim)
            d = np.abs(p[i] - p[fitness.argmax()])
            l[i] = d * np.exp(b * alpha) * np.cos(2 * np.pi * r3) + l[i]

    # 返回最优解
    best_index = fitness.argmin()
    return p[best_index], fitness[best_index]

# 测试
search_space = [-10, 10]
result = whale_optimization_algorithm(function, 1, search_space, 100)
print("最优解：", result[0])
print("最优目标函数值：", result[1])

需要注意的是，这只是一个简单的鲸鱼优化算法实现，实际应用中需要根据具体问题进行调整和优化。