问题描述
我正在尝试使用 openMDAO 的 pyopt-sparse 驱动程序和 NSGA2 算法来解决多目标优化问题.代码如下:
I am trying to solve a multi-objective optimization problem using openMDAO's pyopt-sparse driver with NSGA2 algorithm. Following is the code:
from __future__ import print_function
from openmdao.api import IndepVarComp, Component, Problem, Group, pyOptSparseDriver
class Circ2(Component):
def __init__(self):
super(Circ2, self).__init__()
self.add_param('x', val=10.0)
self.add_param('y', val=10.0)
self.add_output('f1', val=40.0)
self.add_output('f2', val=40.0)
def solve_nonlinear(self, params, unknowns, resids):
x = params['x']
y = params['y']
unknowns['f1'] = (x - 0.0)**2 + (y - 0.0)**2
unknowns['f2'] = (x - 1.0)**2 + (y - 1.0)**2
def linearize(self, params, unknowns, resids):
J = {}
x = params['x']
y = params['y']
J['f1', 'x'] = 2*x
J['f1', 'y'] = 2*y
J['f2', 'x'] = 2*x-2
J['f2', 'y'] = 2*y-2
return J
if __name__ == "__main__":
# Defining Problem & Root
top = Problem()
root = top.root = Group()
# Adding in-present variable values and model
startVal = 50.0
root.add('p1', IndepVarComp('x', startVal))
root.add('p2', IndepVarComp('y', startVal))
root.add('p', Circ2())
# Making Connections
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
# Configuring Driver
top.driver = pyOptSparseDriver()
top.driver.options['optimizer'] = 'NSGA2'
# Setting bounds for the optimizer
top.driver.add_desvar('p1.x', lower=-600, upper=600)
top.driver.add_desvar('p2.y', lower=-600, upper=600)
# Setting Objective Function(s)
top.driver.add_objective('p.f1')
top.driver.add_objective('p.f2')
# # Setting up constraints
# top.driver.add_constraint('con.c', lower=1.0)
top.setup()
top.run()
我收到以下错误 -
Traceback (most recent call last):
File "/home/prasad/DivyaManglam/Python Scripts/Pareto Testing/Basic_Sphere.py", line 89, in <module>
top.run()
File "/usr/local/lib/python2.7/dist-packages/openmdao/core/problem.py", line 1038, in run
self.driver.run(self)
File "/usr/local/lib/python2.7/dist-packages/openmdao/drivers/pyoptsparse_driver.py", line 280, in run
sol = opt(opt_prob, sens=self._gradfunc, storeHistory=self.hist_file)
File "/usr/local/lib/python2.7/dist-packages/pyoptsparse/pyNSGA2/pyNSGA2.py", line 193, in __call__
self.optProb.comm.bcast(-1, root=0)
File "MPI/Comm.pyx", line 1276, in mpi4py.MPI.Comm.bcast (src/mpi4py.MPI.c:108819)
File "MPI/msgpickle.pxi", line 620, in mpi4py.MPI.PyMPI_bcast (src/mpi4py.MPI.c:47164)
File "MPI/msgpickle.pxi", line 143, in mpi4py.MPI.Pickle.load (src/mpi4py.MPI.c:41248)
TypeError: only length-1 arrays can be converted to Python scalars
请告诉您是否可以解决上述错误以及如何修复它.
Kindly tell if you can make anything of the above error and how to fix it.
此外，多目标问题的解决方案的输出将以什么形式返回.我打算为此生成一个帕累托最优前沿.谢谢大家.
Also, in what form would the output of the multi-objective problem's solution be returned. I intend to generate a pareto-optimal front for this.Thank you all.
推荐答案
事实证明这不是 OpenMDAO 错误，而是 NSGA2 的 Pyopt 稀疏包装器中的错误.修复并不可怕，我已经提交了一个 拉取请求 到 pyoptsparse 仓库.与此同时，修补您本地的 pyoptsparse 副本也很容易.
It turns out this is not an OpenMDAO bug, but rather a bug in the Pyopt sparse wrapper for NSGA2. The fix wasn't terrible, and I've submitted a pull request to the pyoptsparse repo for it. In the meantime it would be easy enough to patch your local copy of pyoptsparse.
您询问了如何报告结果.当前的 NSGA2 包装器对结果没有任何作用.它只是让 NSGA2 将它们全部写入一系列文本文件中，例如 nsga2_best_pop.out，如下所示:
You asked how the results will be reported. The current NSGA2 wrapper doesn't do anything with the results. It just lets NSGA2 write them all out to a series of text files such as nsga2_best_pop.out that look like this:
# This file contains the data of final feasible population (if found)
# of objectives = 1, # of constraints = 0, # of real_var = 2, # of bits of bin_var = 0, constr_violation, rank, crowding_distance
-1.000000e+00 3.190310e+01 -2.413640e+02 0.000000e+00 1 1.000000e+14
-1.000000e+00 -5.309160e+02 2.449727e+02 0.000000e+00 1 0.000000e+00
-1.000000e+00 -3.995119e+02 -1.829071e+02 0.000000e+00 1 0.000000e+00
作为旁注，在您的示例中，您为示例中的组件实现了线性化方法.当您需要计算梯度时使用该方法，但 NSGA2 是一个无梯度优化器，根本不会使用它.因此，除非您还计划测试一些基于梯度的方法，否则您可以将该方法排除在组件之外.
As a side note, in your example, you implemented a linearize method for the component in your example. That method is used when you need to compute gradients, but NSGA2 is a gradient free optimizer and won't use it at all. So unless you're also planning to test out some gradient based methods you can leave that method out of your components.
这篇关于使用 NSGA2 解决多目标问题时出现“TypeError".来自 OpenMDAO 1.x 中的 pyopt-sparse的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！