鲸鱼优化算法Python代码

鲸鱼优化算法（Whale Optimization Algorithm, WOA）是一种生物启发式优化算法，模仿了海豚和虎鲸的行为策略。它的核心思想是通过领导者、追随者和探索者的角色，搜索高维空间中的最优解。

以下是简单实现鲸鱼优化算法（WOA）的Python代码示例：

import numpy as np

def position_update(current_position, a, c):
    D = np.random.uniform(0, 2 * a - 1)
    r1 = np.random.rand()
    r2 = np.random.rand()

    A = 2 * a * r1 - a
    C = 2 * r2

    Xl = current_position - A * np.sin(D * np.pi) * abs(C * current_position)

    if np.random.rand() < 0.5:
        Xl += c * (np.random.uniform(-1, 1) * np.random.choice([-1, 1], size=current_position.shape))
    
    return np.clip(Xl, -bound, bound)

class WhaleOptimizationAlgorithm:
    def __init__(self, bounds, population_size, max_iter, a=2, c=2):
        self.bounds = bounds
        self.pop_size = population_size
        self.max_iter = max_iter
        self.a = a
        self.c = c
        self.position = self.init_population(bounds, population_size)
        self.best_solution = None

    def init_population(self, bounds, pop_size):
        # 初始化种群随机位置在给定范围内
        return np.random.uniform(bounds[:, 0], bounds[:, 1], size=(pop_size, len(bounds[0])))

    def update_best_solution(self, new_solution):
        if new_solution is not None and self.best_solution is None or np.all(new_solution < self.best_solution):
            self.best_solution = new_solution

    def optimize(self):
        for _ in range(self.max_iter):
            new_positions = [position_update(position, self.a, self.c) for position in self.position]
            
            # 更新新位置和最佳解决方案
            self.position = new_positions
            self.update_best_solution(np.min(self.position, axis=0))

        return self.best_solution

# 示例用法
bounds = np.array([[-10, 10], [-10, 10]])  # 假设目标函数有两个维度
woa = WhaleOptimizationAlgorithm(bounds, 30, 100)
best_solution = woa.optimize()
print("最优解：", best_solution)

在这个例子中，`position_update` 函数模拟了鲸鱼的游泳行为，而 `optimize` 方法则是整个算法的核心循环。请注意，实际应用时需要将此算法连接到特定的目标函数求解问题。