0

我最近使用 pymoo 来解决离散优化问题。我正在尝试使用差分进化算法来解决问题。按照文档，我尝试了几件事..

import numpy as np

from pymoo.factory import get_algorithm, get_crossover, get_mutation, get_sampling
from pymoo.optimize import minimize
from pymoo.core.problem import Problem


class MyProblem(Problem):

    def __init__(self):
        super().__init__(n_var=2, n_obj=1, n_constr=1, xl=0, xu=10, type_var=int)

    def _evaluate(self, x, out, *args, **kwargs):
        out["F"] = - np.min(x * [3, 1], axis=1)
        out["G"] = x[:, 0] + x[:, 1] - 10


method = get_algorithm("de",pop_size=100,sampling=get_sampling("int_random"),
                       mutation=get_mutation("int_pm", eta=3.0),eliminate_duplicates=True)


res = minimize(MyProblem(),method, seed=1,save_history=True)

print("Best solution found: %s" % res.X)
print("Function value: %s" % res.F)
print("Constraint violation: %s" % res.CV)

并且效果很好。因此推断 pymoo 支持离散变量的 DE。

我正在尝试在pymoo中使用可用于DE算法的示例..

from pymoo.algorithms.soo.nonconvex.de import DE
from pymoo.factory import get_problem
from pymoo.operators.sampling.lhs import LHS
from pymoo.optimize import minimize


problem = get_problem("ackley", n_var=10)


algorithm = DE(
    pop_size=100,
    sampling=LHS(),
    variant="DE/rand/1/bin",
    CR=0.3,
    dither="vector",
    jitter=False
)

res = minimize(problem,
               algorithm,
               seed=1,
               verbose=False)

print("Best solution found: \nX = %s\nF = %s" % (res.X, res.F))

我面临的问题是我不确定type_var在此示例中将参数传递到何处。我尝试过 get_prboblem() 但它说未定义。在文档中找不到它..有人可以帮助我在哪里传递type_var以使 DE 作为离散优化工作吗？

4

0 回答 0