鲸鱼优化算法python

鲸鱼优化算法是一种启发式优化算法，它模拟了鲸鱼的迁徙行为来寻找最优解。以下是使用Python实现鲸鱼优化算法的示例代码：

import random
import math

# 定义目标函数
def func(x):
    return math.sin(10 * x) * x + math.cos(2 * x) * x

# 初始化种群
def init_population(pop_size, x_min, x_max):
    population = []
    for i in range(pop_size):
        x = random.uniform(x_min, x_max)
        population.append(x)
    return population

# 计算适应度值
def fitness(x):
    return func(x)

# 鲸鱼优化算法
def WOA(pop_size, max_iter, x_min, x_max):
    # 初始化种群
    population = init_population(pop_size, x_min, x_max)
    # 迭代寻优
    for t in range(max_iter):
        a = 2 - 2 * t / max_iter  # 更新参数a
        # 遍历每个个体
        for i in range(pop_size):
            # 随机选择两个个体
            r1 = random.randint(0, pop_size - 1)
            r2 = random.randint(0, pop_size - 1)
            # 计算距离
            A = 2 * a * random.random() - a
            C = 2 * random.random()
            l = random.uniform(-1, 1)
            p = random.random()
            D = abs(C * population[r1] - population[r2])
            X1 = population[r1] - A * D
            if p < 0.5:
                if abs(A) >= 1:
                    Xnew = X1 - l * D
                else:
                    Xnew = population[i] - A * D
            else:
                X2 = population[i] - A * D
                if fitness(X2) < fitness(population[i]):
                    Xnew = X2
                else:
                    Xnew = X1 - l * D
            # 边界处理
            if Xnew < x_min:
                Xnew = x_min
            elif Xnew > x_max:
                Xnew = x_max
            # 更新个体
            population[i] = Xnew
    # 返回最优解
    best_x = population[0]
    best_fitness = fitness(population[0])
    for i in range(1, pop_size):
        if fitness(population[i]) < best_fitness:
            best_x = population[i]
            best_fitness = fitness(population[i])
    return best_x, best_fitness

# 示例
x_min = -5
x_max = 5
pop_size = 30
max_iter = 100
best_x, best_fitness = WOA(pop_size, max_iter, x_min, x_max)
print("Best solution: x = %.4f, f(x) = %.4f" % (best_x, best_fitness))

在示例中，我们定义了目标函数`func()`，并使用`init_population()`初始化种群。然后，我们使用`WOA()`函数实现鲸鱼优化算法，并返回最优解。最后，我们输出最优解的结果。可以根据实际需求调整参数值。