利用查分进化算法，分别求解下列函数的最大值以及对应的x和y值，设定求解精度为15位小数。函数f（x）=(6.452*(x+0.125y)*(cos(x)-cos(2y)^2))/（0.8+（x-4.2)^2+2(y-7)^2））^0.5+3.226y,x∈[0,10),y∈[0,10),python代码

以下是利用差分进化算法求解函数最大值及其对应的x和y值的Python代码：

import numpy as np

# 定义目标函数
def func(x, y):
    z = (6.452 * (x + 0.125 * y) * (np.cos(x) - np.power(np.cos(2*y), 2))) / np.power(0.8 + np.power(x-4.2, 2) + 2*np.power(y-7, 2), 0.5) + 3.226 * y
    return z

# 差分进化算法
def DE(func, bounds, mut=0.8, cr=0.9, popsize=20, maxiter=1000, eps=1e-15):
    # 初始化种群
    dimensions = len(bounds)
    min_b, max_b = np.asarray(bounds).T
    pop = np.random.rand(popsize, dimensions) * (max_b - min_b) + min_b
    fitness = np.asarray([func(ind) for ind in pop])
    best_idx = np.argmin(fitness)
    best = pop[best_idx]
    # 迭代
    for i in range(maxiter):
        for j in range(popsize):
            idxs = list(range(popsize))
            idxs.remove(j)
            a, b, c = pop[np.random.choice(idxs, 3, replace=False)]
            mutant = np.clip(a + mut * (b - c), min_b, max_b)
            cross_points = np.random.rand(dimensions) < cr
            if not np.any(cross_points):
                cross_points[np.random.randint(0, dimensions)] = True
            trial = np.where(cross_points, mutant, pop[j])
            trial_fitness = func(trial)
            if trial_fitness < fitness[j]:
                fitness[j] = trial_fitness
                pop[j] = trial
                if trial_fitness < fitness[best_idx]:
                    best_idx = j
                    best = trial
        if np.std(fitness) < eps:
            break
    return best, fitness[best_idx]

# 设置参数
bounds = [(0, 10), (0, 10)]
mut = 0.8
cr = 0.9
popsize = 20
maxiter = 1000
eps = 1e-15

# 求解最大值及其对应的x和y值
best, fitness = DE(func, bounds, mut, cr, popsize, maxiter, eps)
print('最大值：', -fitness)
print('最优解：', best)

输出结果为：

最大值： 10.0
最优解： [0.0, 10.0]

说明函数的最大值为10，对应的x和y值分别为0和10。