0

我只是盯着 OpenMDAO 和多学科优化，我将使用 OpenMDAO 为经典的Sellar问题构建一个 CO（协同优化）框架。

但是，当我运行此代码时，我总是收到以下错误：

类型错误：_init_sys_data() 缺少 1 个必需的位置参数：'probdata'

运行时发生这种情况problem.setup()
我不知道这个错误是什么意思，因为这行代码与 OpenMDAO 标准 tut 没有区别。
有人可以给我一些建议吗？

这是代码，来自这个问题的一些提示

以 CO 形式形成的 Sellar 问题：

    SystemOpt
    min     x1**2  + z2 + y1 + eye(-y2)
    w.r.t   z1, z2, x1, y1, y2  
    s.t     (z1 - z1_d1)**2 + (x1 - x1_d1)**2 + (z2 - z2_d1)**2 + 0.2 * (y2 - y2_d1) <= epsilon
            (y2 - y2_d2)**2 + (y1 - y1_d2)**2 + (z1 - z1_d2)**2 + (z2 - z2_d2)**2 <= epsilon
            -10 <= z1 <= 10
            0 <= z2 <= 10
            0 <= x1 <= 10


    SobOpt1
        min     (z1 - z1_d1)**2 + (x1 - x1_d1)**2 + (z2 - z2_d1)**2 + 0.2 * (y2 - y2_d1)
        w.r.t   z1_d1, x1_d1, z2_d1, y2_d1
        s.t     z1_d1**2 + x1_d1 + z2_d1 - 0.2 * y2_d1 >= 3.16
                0 <= x1_d1 <=10
                -10 <= z1_d1 <= 10
                0 <= z2_d1 <= 10



    SubOpt2
        min     (y2 - y2_d2)**2 + (y1 - y1_d2)**2 + (z1 - z1_d2)**2 + (z2 - z2_d2)**2
        w.r.t   y2_d2, y1_d2, z1_d2, z2_d2
        s.t     y1_d2 ** 0.5 + z1_d2 + z2_d2 <= 24
                -10 <= z1_d2 <= 10
                0 <= z2_d2 <= 10

====================================================

from __future__ import print_function
import numpy as np
from openmdao.api import ExecComp, IndepVarComp, Group
from openmdao.api import Component, ScipyOptimizer

class Discipline1(Component):
    """Component containing Discipline 1."""

    def __init__(self):
        super(Discipline1, self).__init__()

        self.add_param('z1_d1', val=0.0)
        self.add_param('x1_d1', val=0.)
        self.add_param('z2_d1', val=1.0)
        self.add_param('y2_d1', val=1.0)
        self.add_param('z1', val=1.0)
        self.add_param('x1', val=1.0)
        self.add_param('z2', val=1.0)
        self.add_param('y2', val=1.0)
        self.add_param('obj1', val=1.0)

    def solve_nonlinear(self, params, unknowns, resids):
        """
        Evaluates the equation
        y1 = z1**2 + z2 + x1 - 0.2*y2
        """

        z1_d1 = params['z']
        z2_d1 = params['z2_d1']
        x1_d1 = params['x1_d1']
        y2_d1 = params['y2_d1']
        z1 = params['z1']
        x1 = params['x2']
        z2 = params['z2']
        y2 = params['y2']

        unknowns['obj1'] = (z1 - z1_d1) ** 2 + (x1 - x1_d1) ** 2 + (z2 - z2_d1) ** 2 + 0.2 * (y2 - y2_d1)


class SubOpt1(Component):
    def __init__(self):
        """
        Sobopt of discipline1
        """
        super(SubOpt1, self).__init__()
        self.add_param('S1px', IndepVarComp('x1_d1', 1.0), promotes=['x1_d1'])
        self.add_param('S1pz', IndepVarComp('z1_d1', 5.0), promotes=['z1_d1'])
        self.add_param('S1pz', IndepVarComp('z2_d1', 5.0), promotes=['z2_d1'])
        self.add_param('S1py2', IndepVarComp('y2_d1', 2.0), promotes=['y2_d1'])
        # Add Problem
        from openmdao.api import Problem
        self.problem = prob = Problem()
        group = prob.root = Group()
        # add Component:
        group.add('Discipline', Discipline1,
                  promotes=['x1', 'z1', 'x2', 'y2', 'x1_d1', 'z1_d1', 'x2_d2', 'y2_d2', 'obj1'])
        # Add Cons
        group.add('con', ExecComp('con = z1_d1**2 + x1_d1 + z2_d1 - 0.2 * y2_d1'),
                  promotes=['con', 'z1_d1', 'x1_d1', 'z2_d1', 'y2_d1'])
        # Add Solver
        prob.driver = ScipyOptimizer()
        prob.driver.options['optimizer'] = 'SLSQP'
        prob.driver.options['tol'] = 1.0e-8

        # Add desvar
        prob.driver.add_desvar('x1_d1', lower=0.0, upper=10.0)
        prob.driver.add_desvar('z1_d1', lower=-10.0, upper=10.0)
        prob.driver.add_desvar('z2_d1', lower=0.0, upper=10.0)
        prob.driver.add_desvar('y2_d1')

        # Add obj and cons
        prob.driver.add_objective('obj1')
        prob.driver.add_constraint('con', lower=3.16)

        prob.setup()

    def solve_nonlinear(self, params=None, unknowns=None, resids=None, metadata=None):
        self.problem.run()
        unknowns['obj1'] = self.problem['obj1']


class Discipline2(Component):
    """Component containing Discipline 1."""

    def __init__(self):
        super(Discipline2, self).__init__()

        self.add_param('z1_d2', val=0.0)
        self.add_param('z2_d2', val=0.)
        self.add_param('y1_d2', val=1.0)
        self.add_param('y2_d2', val=1.0)
        self.add_param('z1', val=1.0)
        self.add_param('y1', val=1.0)
        self.add_param('z2', val=1.0)
        self.add_param('y2', val=1.0)
        # add objs
        self.add_param('obj2', val=1.0)

    def solve_nonlinear(self, params, unknowns, resids):
        """
        Evaluates the equation
        y1 = z1**2 + z2 + x1 - 0.2*y2
        """

        z1_d2 = params['z1_d2']
        z2_d2 = params['z2_d2']
        y1_d2 = params['y1_d2']
        y2_d2 = params['y2_d2']
        z1 = params['z1']
        y1 = params['y2']
        z2 = params['z2']
        y2 = params['y2']

        unknowns['obj2'] = (y2 - y2_d2) ** 2 + (y1 - y1_d2) ** 2 + (z1 - z1_d2) ** 2 + (z2 - z2_d2) ** 2


class SubOpt2(Component):
    def __init__(self):
        """
        Subopt of discipline2
        """
        super(SubOpt2, self).__init__()
        # Add Desvar
        self.add_param('S2pz', IndepVarComp('z1_d2', 5.0), promotes=['z1_d2'])
        self.add_param('S2pz', IndepVarComp('z2_d2', 5.0), promotes=['z2_d2'])
        self.add_param('S2py1', IndepVarComp('y1_d2', 2.0), promotes=['y1_d2'])
        self.add_param('S2py2', IndepVarComp('y2_d2', 5.0), promotes=['y2_d2'])

        # Add problem
        from openmdao.api import Problem
        self.problem = prob = Problem()
        group = prob.root = Group()
        # Add Component:
        group.add('Discipline2', Discipline2,
                  promotes=['obj2', 'y2', 'y2_d2', 'y1', 'y1_d2', 'z1', 'z1_d2', 'z2', 'z2_d2'])
        # Add cons:
        group.add('con2', ExecComp('con = y1_d2 ** 0.5 + z1_d2 + z2_d2'),
                  promotes=['con', 'y1_d2', 'z1_d2', 'z2_d2'])
        # Add solver:
        prob.driver = ScipyOptimizer()
        prob.driver.options['optimizer'] = 'SLSQP'
        prob.driver.options['tol'] = 1.0e-8

        # Add desvar
        prob.driver.add_desvar('y1_d2')
        prob.driver.add_desvar('y2_d2')
        prob.driver.add_desvar('z1_d2', lower=-10, upper=10)
        prob.driver.add_desvar('z2_d2', lower=0.0, upper=10.0)

        # Add ovj and cons
        prob.driver.add_objective('obj2')
        prob.driver.add_constraint('con2', upper=24)

        prob.setup()

    def solve_nonlinear(self, params=None, unknowns=None, resids=None, metadata=None):
        self.problem.run()


class System_Opt(Group):
    """
    Group containing the Sellar MDA. This version uses the disciplines
    with derivatives."""

    def __init__(self):
        super(System_Opt, self).__init__()

        self.add('px', IndepVarComp('x1', 1.0), promotes=['x1'])
        self.add('pz1', IndepVarComp('z1', 5.0), promotes=['z1'])
        self.add('pz2', IndepVarComp('z2', 5.0), promotes=['z2'])
        self.add('py1', IndepVarComp('y1', 2.0), promotes=['y1'])
        self.add('py2', IndepVarComp('y2', 2.0), promotes=['y2'])

        self.add('obj_cmp', ExecComp('obj = x1**2  + z2 + y1 + eye(-y2)',
                                     z2=0.0, x1=0.0, y1=0.0, y2=0.0),
                 promotes=['obj', 'z2', 'x1', 'y1', 'y2'])

        self.add('SubOpt1', SubOpt1, promotes=['z1_d1', 'x1_d1', 'z2_d1', 'y2_d1', 'x1', 'z1', 'x2', 'y2'])
        self.add('SubOpt2', SubOpt2, promotes=['y2_d2', 'y1_d2', 'z1_d2', 'z2_d2', 'y2', 'y1', 'z1', 'z2'])

        self.add('con_cmp1',
                 ExecComp('con1 = (z1 - z1_d1)**2 + (x1 - x1_d1)**2 + (z2 - z2_d1)**2 + 0.2 * (y2 - y2_d1)'),
                 promotes=['con1', 'z1', 'z1_d1', 'x1', 'x1_d1', 'z2', 'z2_d1', 'y2', 'y2_d1'])
        self.add('con_cmp2',
                 ExecComp('con2 = (y2 - y2_d2)**2 + (y1 - y1_d2)**2 + (z1 - z1_d2)**2 + (z2 - z2_d2)**2'),
                 promotes=['con2', 'y2', 'y2_d2', 'y1', 'y1_d2', 'z1', 'z1_d2', 'z2', 'z2_d2'])

if __name__ == '__main__':
    epsilon = 1e-5
    from openmdao.api import Problem, ScipyOptimizer

    # Add problem
    top = Problem()
    top.root = System_Opt()
    # Add solver
    top.driver = ScipyOptimizer()
    top.driver.options['optimizer'] = 'SLSQP'
    top.driver.options['tol'] = 1.0e-8

    # Add desvar
    top.driver.add_desvar('x1', lower=0.0, upper=10.0)
    top.driver.add_desvar('z1', lower=-10.0, upper=10.0)
    top.driver.add_desvar('z2', lower=0.0, upper=10.0)
    top.driver.add_desvar('y2')
    top.driver.add_desvar('y1')

    # Add obj and cons
    top.driver.add_objective('obj')
    top.driver.add_constraint('con1', upper=epsilon)
    top.driver.add_constraint('con2', upper=epsilon)

    top.setup()

    # add init params of desvar
    top['x1'] = 1.0
    top['z1'] = 1.0
    top['z2'] = 1.0
    top['y2'] = 1.0
    top['y1'] = 1.0
    top.run()
4

1 回答 1

3

您遇到的问题与以下几行有关： self.add('SubOpt1', SubOpt1, promotes=['z1_d1', 'x1_d1', 'z2_d1', 'y2_d1', 'x1', 'z1', 'x2', 'y2']) self.add('SubOpt2', SubOpt2, promotes=['y2_d2', 'y1_d2', 'z1_d2', 'z2_d2', 'y2', 'y1', 'z1', 'z2'])

请注意，这里您将类传递给 add 方法，而不是实例。您也在 SubOpt1 和 SubOpt2 中做了同样的事情

group.add('Discipline', Discipline1, promotes=['x1', 'z1', 'x2', 'y2', 'x1_d1', 'z1_d1', 'x2_d2', 'y2_d2', 'obj1']) # Add Cons

相反，您需要将实例传递给它们，如下所示：

self.add('SubOpt1', SubOpt1(), promotes=['z1_d1', 'x1_d1', 'z2_d1', 'y2_d1', 'x1', 'z1', 'x2', 'y2']) self.add('SubOpt2', SubOpt2(), promotes=['y2_d2', 'y1_d2', 'z1_d2', 'z2_d2', 'y2', 'y1', 'z1', 'z2'])

您的脚本中还有一些与变量名相关的小错误，但类与实例问题是您的主要问题

于 2017-07-04T12:24:35.997 回答