整数的Scipy微分进化 [英] Scipy Differential Evolution with integers

问题描述

我正在尝试使用scipy.optimize.differential_evolution进行优化.该代码要求x中每个变量的范围.但是我想要一个解决方案，其中x的部分必须是整数，而其他部分则可以自由浮动.我的代码的相关部分看起来像

I'm trying to run an optimization with scipy.optimize.differential_evolution. The code calls for bounds for each variable in x. But I want to a solution where parts of x must be integers, while others can range freely as floats. The relevant part of my code looks like

    bounds = [(0,3),(0,3),(0,3),???,???]
    result = differential_evolution(func, bounds)

我该如何替换???，以强制这些变量在给定范围内为整数?

What do I replace the ???'s with to force those variables to be ints in a given range?

推荐答案

如注释中所述，不直接支持整数约束".

As noted in the comments there isn't direct support for a "integer constraint".

不过，您可以最小化修改后的目标函数，例如:

You could however minimize a modified objective function, e.g.:

def func1(x):
    return func(x) + K * (x[3] - round(x[3]))**2

，这将迫使x[3]接近整数值(不幸的是，您必须调整K参数).

and this will force x[3] towards an integer value (unfortunately you have to tune the K parameter).

另一种方法是在评估目标函数之前对(某些)实值参数进行四舍五入:

An alternative is to round (some of) the real-valued parameters before evaluating the objective function:

def func1(x):
    z = x;
    z[3] = round(z[3])
    return func(z)

这两种都是使用差分进化方法解决离散优化问题的常用技术，并且效果很好.

Both are common techniques to approach a discrete optimization problem using Differential Evolution and they work quite well.

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