我只是盯着OpenMDAO和多学科优化，我将使用OpenMDAO来构建关于经典Sellar问题的CO（协作优化）框架。
但是，当我运行此代码时，我总是会收到以下错误：
运行TypeError：_init_sys_data（）缺少1个必需的位置参数：'probdata'
problem.setup()时发生
我不知道这个错误意味着什么，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？
以下是代码，来自this question
的一些提示塞尔拉问题形成为CO：
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()
答案 0 :(得分：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，您在此处将类传递给add方法，而不是实例。你也在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，您需要按如下方式将实例传递给它们：
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称相关的小错字，但类vs实例问题是您的主要问题