鲸鱼算法的悬架适应度函数代码

鲸鱼算法的适应度函数代码因不同的问题而异，以下是一个简单的示例，适用于求解一个多变量函数的最小值：

import numpy as np

def objective_function(x):
    # 计算多变量函数的值，例如 Rosenbrock 函数
    n = len(x)
    sum = 0
    for i in range(n-1):
        sum += 100 * (x[i+1] - x[i]**2)**2 + (x[i] - 1)**2
    return sum

def fitness_function(population):
    # 计算种群中每个个体的适应度值
    fitness_values = []
    for i in range(len(population)):
        fitness_values.append(objective_function(population[i]))
    return fitness_values

def whale_algorithm(obj_function, dim, search_space, max_iter, population_size):
    # 鲸鱼算法主程序
    # obj_function: 目标函数
    # dim: 变量个数
    # search_space: 变量的搜索范围
    # max_iter: 最大迭代次数
    # population_size: 种群大小
    best_solution = None
    best_fitness = np.inf
    population = np.random.uniform(low=search_space[0], high=search_space[1], size=(population_size, dim))
    fitness_values = fitness_function(population)
    for iter in range(max_iter):
        a = 2 - 2 * iter / (max_iter - 1)  # 控制参数a的值
        for i in range(population_size):
            r_1 = np.random.rand(dim)  # 随机向量
            r_2 = np.random.rand(dim)  # 随机向量
            A = 2 * a * r_1 - a  # 更新系数A
            C = 2 * r_2  # 更新系数C
            l = np.random.uniform(low=-1, high=1)  # 选择一个随机值l
            p = np.random.rand()  # 选择一个随机值p
            if p < 0.5:
                if abs(A[0]) < 1:
                    D = abs(C * best_solution - population[i])  # 向最优解靠近
                    new_solution = best_solution - A * D  # 更新个体位置
                else:
                    rand_index = np.random.randint(0, population_size)  # 随机选择一个个体
                    X_rand = population[rand_index]
                    D = abs(C * X_rand - population[i])  # 向随机个体靠近
                    new_solution = X_rand - A * D  # 更新个体位置
            else:
                D = abs(best_solution - population[i])  # 向最优解靠近
                new_solution = best_solution - A * D - l * D  # 更新个体位置
            # 边界处理
            new_solution = np.clip(new_solution, search_space[0], search_space[1])
            # 比较新解的适应度值与旧解的适应度值
            new_fitness = obj_function(new_solution)
            if new_fitness < fitness_values[i]:
                fitness_values[i] = new_fitness
                population[i] = new_solution
        # 更新全局最优解
        best_index = np.argmin(fitness_values)
        if fitness_values[best_index] < best_fitness:
            best_fitness = fitness_values[best_index]
            best_solution = population[best_index]
    return best_solution, best_fitness

在上述代码中，`objective_function` 函数用于计算多变量函数的值，`fitness_function` 函数用于计算种群中每个个体的适应度值。`whale_algorithm` 函数是鲸鱼算法的主程序，其中 `a` 是控制参数，随着迭代次数的增加逐渐减小，用于控制搜索范围的缩小。在每次迭代中，对于每个个体，算法会根据一定的规则来更新其位置，然后比较新位置与旧位置的适应度值，选择更优者作为个体的新位置。同时，算法还会更新全局最优解。最终，算法返回全局最优解及其适应度值。