scipy.optimize.differential_evolution 计算要最小化的函数多少次？

Question

我正在尝试应用 this answer to my code to display a progress bar for the scipy.optimize.differential_evolution 方法。

我以为differential_evolution会计算func（被调用的函数被最小化）popsize * maxiter次，但显然不是这样。

下面的代码应该显示一个进度条，该进度条会增加到 100%:

[####################] 100%

但实际上这会继续进行，因为 DEdist() 函数的计算次数比 popsize * maxiter 多得多（我将其用作 updt() 的 total 参数功能）。

如何计算 differential_evolution 执行的函数求值总数？这完全可以做到吗？

from scipy.optimize import differential_evolution as DE
import sys


popsize, maxiter = 10, 50


def updt(total, progress, extra=""):
    """
    Displays or updates a console progress bar.

    Original source: 
    """
    barLength, status = 20, ""
    progress = float(progress) / float(total)
    if progress >= 1.:
        progress, status = 1, "\r\n"
    block = int(round(barLength * progress))
    text = "\r[{}] {:.0f}% {}{}".format(
        "#" * block + "-" * (barLength - block),
        round(progress * 100, 0), extra, status)
    sys.stdout.write(text)
    sys.stdout.flush()


def DEdist(model, info):
    updt(popsize * maxiter, info['Nfeval'] + 1)
    info['Nfeval'] += 1

    res = (1. - model[0])**2 + 100.0 * (model[1] - model[0]**2)**2 + \
        (1. - model[1])**2 + 100.0 * (model[2] - model[1]**2)**2

    return res


bounds = [[0., 10.], [0., 10.], [0., 10.], [0., 10.]]
result = DE(
    DEdist, bounds, popsize=popsize, maxiter=maxiter,
    args=({'Nfeval': 0},))

Answer 1

来自help(scipy.optimize.differential_evolution)：

maxiter : int, optional
    The maximum number of generations over which the entire population is
    evolved. The maximum number of function evaluations (with no polishing)
    is: ``(maxiter + 1) * popsize * len(x)``

默认也polish=True：

polish : bool, optional
    If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
    method is used to polish the best population member at the end, which
    can improve the minimization slightly.

所以你需要改变两件事：

1 此处使用正确的公式：

updt(popsize * (maxiter + 1) * len(model), info['Nfeval'] + 1)

2 传递 polish=False 参数：

result = DE(
    DEdist, bounds, popsize=popsize, maxiter=maxiter, polish=False,
    args=({'Nfeval': 0},))

之后你会看到进度条正好在达到 100% 时停止。

scipy.optimize.differential_evolution 计算要最小化的函数多少次？

How many times does scipy.optimize.differential_evolution evaluate the function to be minimized?

python

mathematical-optimization

scipy

output