鲸鱼算法python实例

当然，我可以为您提供一个简单的鲸鱼算法的 Python 示例。鲸鱼算法是一种启发式优化算法，受到鲸鱼迁徙行为的启发。以下是一个简单的实现示例：

import random
import math

# 定义问题的目标函数，这里以 Rosenbrock 函数为例
def objective_function(x, y):
    return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2

# 定义鲸鱼算法主函数
def whale_algorithm(iterations, population_size, search_space):
    population = []
    best_solution = None
    best_fitness = float('inf')

    # 初始化种群
    for _ in range(population_size):
        x = random.uniform(search_space[0], search_space[1])
        y = random.uniform(search_space[0], search_space[1])
        solution = [x, y]
        fitness = objective_function(x, y)
        population.append((solution, fitness))

        # 更新最优解
        if fitness < best_fitness:
            best_solution = solution
            best_fitness = fitness

    # 开始迭代
    for _ in range(iterations):
        a = 2 - 2 * (_ / iterations)  # 更新 a 值

        for i in range(population_size):
            x, y = population[i][0]

            # 随机选择一条路径
            r1 = random.random()
            r2 = random.random()
            A = 2 * a * r1 - a
            C = 2 * r2

            # 更新位置
            D = abs(C * best_solution[0] - x)
            new_x = best_solution[0] - A * D

            r1 = random.random()
            r2 = random.random()
            A = 2 * a * r1 - a
            C = 2 * r2

            D = abs(C * best_solution[1] - y)
            new_y = best_solution[1] - A * D

            # 边界处理
            new_x = max(search_space[0], min(new_x, search_space[1]))
            new_y = max(search_space[0], min(new_y, search_space[1]))

            # 计算适应度
            new_fitness = objective_function(new_x, new_y)

            # 更新最优解
            if new_fitness < best_fitness:
                best_solution = [new_x, new_y]
                best_fitness = new_fitness

            # 更新种群
            population[i] = ([new_x, new_y], new_fitness)

    return best_solution, best_fitness

# 测试鲸鱼算法
iterations = 100  # 迭代次数
population_size = 50  # 种群大小
search_space = (-5, 5)  # 搜索空间范围

best_solution, best_fitness = whale_algorithm(iterations, population_size, search_space)
print("Best solution:", best_solution)
print("Best fitness:", best_fitness)

这个示例中，我们使用 Rosenbrock 函数作为目标函数。您可以按照自己的需求修改目标函数和搜索空间。祝您成功使用鲸鱼算法进行优化！