使用Pymoo重新启动优化

Question

我正在尝试重新启动pymoo中的优化。

我有一个问题被定义为：

class myOptProb(Problem):
    """my body goes here"""

algorithm = NSGA2(pop_size=24)  

problem = myOptProblem(opt_obj=dp_ptr,
                       nvars=7,
                       nobj=4,
                       nconstr=0,
                       lb=0.3 * np.ones(7),
                       ub=0.7 * np.ones(7),
                       parallelization=('threads', cpu_count(),))

res = minimize(problem,
               algorithm,
               ('n_gen', 100),
               seed=1,
               verbose=True)

在优化过程中，我将设计矢量和结果写入.csv文件。design_vectors.csv的一个例子是：

5.000000000000000000e+00, 4.079711567060104183e-01, 6.583544872784267143e-01, 4.712364759485179189e-01, 6.859360188593541796e-01, 5.653765991273791425e-01, 5.486782880836487131e-01, 5.275405748345924906e-01,
7.000000000000000000e+00, 5.211287914743063521e-01, 6.368123569438421949e-01, 3.496693260479644128e-01, 4.116734716044557763e-01, 5.343037085833151068e-01, 6.878382993278697732e-01, 5.244120877022839800e-01, 
9.000000000000000000e+00, 5.425317846613321171e-01, 5.275405748345924906e-01, 4.269449637288642574e-01, 6.954464617649794844e-01, 5.318980876983187001e-01, 4.520564690494201510e-01, 5.203792876471586837e-01, 
1.100000000000000000e+01, 4.579502451694219545e-01, 6.853050113762846340e-01, 3.695822666721857441e-01, 3.505318077758549089e-01, 3.540316632186925050e-01, 5.022648662707586142e-01, 3.086099221096791911e-01, 
3.000000000000000000e+00, 4.121775968257620493e-01, 6.157117313805953174e-01, 3.412904026310568106e-01, 4.791574104703620329e-01, 6.634382012372381787e-01, 4.174456593494717538e-01, 4.151101354345394512e-01, 

results.csv为：

5.000000000000000000e+00, 1.000000000000000000e+05, 1.000000000000000000e+05, 1.000000000000000000e+05, 1.000000000000000000e+05, 
7.000000000000000000e+00, 1.041682833582066703e+00, 3.481167125962069189e-03, -5.235115318709097909e-02, 4.634480813876099177e-03, 
9.000000000000000000e+00, 1.067730307802263967e+00, 2.194702810002167534e-02, -3.195892023664552717e-01, 1.841232582360878426e-03, 
1.100000000000000000e+01, 8.986880344052742275e-01, 2.969022150977750681e-03, -4.346692726475211849e-02, 4.995468429444801205e-03, 
3.000000000000000000e+00, 9.638770499257821589e-01, 1.859596479928402393e-02, -2.723230073142696162e-01, 1.600910928983005632e-03, 

第一列是设计向量的索引-因为我是异步线程，所以我指定了索引。

我看到应该可以通过pymoo.algorithms.nsga2.NSGA2的采样参数重新启动优化，但我找不到可用的示例。关于人口和个人的文档也不清楚。那么，如何使用先前的结果重新启动模拟？

Answer 1

是的，你可以用一个种群来初始化algorithm对象，而不是随机执行。

我为有偏差的初始化写了一个小教程：https://pymoo.org/customization/initialization.html

因为在您的示例中，数据已经存在于CSV或内存文件中，您可能希望创建一个虚拟问题(在我的示例中称为Constant )来设置Population对象中的属性。(在population中，需要设置X、F、G、CV和feasible )。另一种方法是直接设置属性...

带有虚拟问题的偏置初始化如下所示。如果您已经使用pymoo存储csv文件，也可以直接np.save Population对象并加载它。那么所有的中间步骤都是不必要的。

我计划在未来改进检查点的实现。所以，如果你有更多的反馈和用例，这些都是不可能的，请让我知道。

import numpy as np

from pymoo.algorithms.nsga2 import NSGA2
from pymoo.algorithms.so_genetic_algorithm import GA
from pymoo.factory import get_problem, G1, Problem
from pymoo.model.evaluator import Evaluator
from pymoo.model.population import Population
from pymoo.optimize import minimize


class YourProblem(Problem):

    def __init__(self, n_var=10):
        super().__init__(n_var=n_var, n_obj=1, n_constr=0, xl=-0, xu=1, type_var=np.double)

    def _evaluate(self, x, out, *args, **kwargs):
        out["F"] = np.sum(np.square(x - 0.5), axis=1)


problem = YourProblem()

# create initial data and set to the population object - for your this is your file
N = 300
X = np.random.random((N, problem.n_var))
F = np.random.random((N, problem.n_obj))
G = np.random.random((N, problem.n_constr))


class Constant(YourProblem):

    def _evaluate(self, x, out, *args, **kwargs):
        out["F"] = F
        out["G"] = G


pop = Population().new("X", X)
Evaluator().eval(Constant(), pop)

algorithm = GA(pop_size=100, sampling=pop)

minimize(problem,
         algorithm,
         ('n_gen', 10),
         seed=1,
         verbose=True)