白鲸优化算法python代码

以下是一个简单的白鲸优化算法的 Python 实现示例：

import numpy as np

# 定义目标函数
def objective_function(x):
    return np.sum(np.square(x))

# 定义白鲸优化算法
def whale_optimization_algorithm(obj_func, num_iterations=100, num_whales=10, search_space=(-100, 100), a=2):
    # 初始化鲸群
    search_min, search_max = search_space
    dimensions = len(search_min)
    whales = []
    for i in range(num_whales):
        whale = np.random.uniform(search_min, search_max, dimensions)
        whales.append(whale)
    # 开始优化
    for t in range(num_iterations):
        # 计算当前最优解
        current_best_whale = None
        current_best_score = None
        for whale in whales:
            score = obj_func(whale)
            if current_best_score is None or score < current_best_score:
                current_best_score = score
                current_best_whale = whale
        # 遍历所有鲸
        for i, whale in enumerate(whales):
            # 随机选择一个领袖鲸
            leader_index = np.random.randint(num_whales)
            leader = whales[leader_index]
            # 计算距离和方向
            distance = np.abs(leader - whale)
            direction = leader - whale
            # 更新位置
            a = 2 - t * ((2) / num_iterations)  # 动态线性降温参数
            b = 1
            l = np.random.uniform(-1, 1, dimensions)
            p = np.random.uniform(0, 1, dimensions)
            A = a * np.multiply(np.multiply(2 * b, p) - b, np.abs(a * current_best_whale - whale))
            C = np.multiply(2 * l, distance)
            new_whale = whale + A - C
            # 边界处理
            new_whale = np.clip(new_whale, search_min, search_max)
            # 更新鲸
            whales[i] = new_whale
    return current_best_whale, current_best_score

使用示例：

search_min = np.array([-5, -5, -5])
search_max = np.array([5, 5, 5])
best_whale, best_score = whale_optimization_algorithm(objective_function, num_iterations=100, num_whales=10, search_space=(search_min, search_max))

print('Best solution: ', best_whale)
print('Best objective function value: ', best_score)

注：以上代码仅作为示例，实际应用中可能需要根据具体情况进行调整和优化。