def traj_optim_static(paths, tree):
path, envs, modes, mnps = paths
guard_index = [0]
n = len(modes)
v_init = np.zeros((n, 3))
for i in range(1, n):
if not np.all(modes[i] == modes[i - 1]):
guard_index.append(i)
elif len(envs[i]) != 0:
if not envs[i][0].is_same(envs[i - 1][0]):
guard_index.append(i)
elif not (mnps[i][0].is_same(mnps[i - 1][0])
and mnps[i][1].is_same(mnps[i - 1][1])):
# manipulator change
guard_index.append(i)
g_v = np.identity(3)
g_v[0:2, 0:2] = config2trans(path[i - 1])[0:2, 0:2]
v_init[i - 1] = np.dot(g_v.T,
np.array(path[i]) - np.array(path[i - 1]))
#guard_index.append(len(modes)-1)
guard_index = np.unique(guard_index)
Gs = dict()
hs = dict()
As = dict()
bs = dict()
for i in range(len(path)):
G, h, A, b = contact_mode_constraints(path[i], mnps[i], envs[i],
modes[i], tree.world,
tree.mnp_mu, tree.env_mu,
tree.mnp_fn_max)
gid = np.any(G[:, 0:3], axis=1)
aid = np.any(A[:, 0:3], axis=1)
Gs[i] = G[gid, 0:3]
hs[i] = h[gid].flatten()
As[i] = A[aid, 0:3]
bs[i] = b[aid].flatten()
modeconstraints = (Gs, hs, As, bs)
q_goal = np.array(tree.x_goal)
opt_prob = Optimization('Trajectory Optimization', obj_fun)
x_init = np.hstack((np.array(path).flatten(), v_init.flatten()))
cs = constraints(x_init, path, Gs, hs, As, bs, guard_index)
opt_prob.addVarGroup('x', n * 6, 'c', value=x_init, lower=-10, upper=10)
opt_prob.addObj('f')
opt_prob.addConGroup('g', len(cs), 'i', lower=0.0, upper=10000.0)
print(opt_prob)
slsqp = SLSQP()
#slsqp.setOption('IPRINT', -1)
slsqp(opt_prob,
sens_type='FD',
goal=q_goal,
path=path,
modecons=modeconstraints,
guard_index=guard_index)
print(opt_prob.solution(0))
qs = [opt_prob.solution(0)._variables[i].value for i in range(n * 3)]
return qs
def solveOpt(int_domain, J, a, model, u0, sign):
'''
INPUT:
int_domain =
J =
a =
model =
u0 =
sign =
OUTPUT:
opt_prob =
'''
def objfun(u, **kwargs):
'''objfun defines the objective function and the constraints (equality
and inequality) of the optiization problem'''
# 1) extract paraeters
int_domain = kwargs['int_domain']
J = kwargs['J']
x = kwargs['a']
model = kwargs['model']
sign = kwargs['sign']
# 2) define objective function
funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x))
g = [0] * 2
# 3) budget constraint
g[0] = u.sum() - 1
# 4) VaR constarint
W = model.W
sigmaMax = model.VaR / norm.ppf(1 - model.alpha)
g[1] = -sigmaMax + np.sqrt(W.dot(u).dot(u))
fail = 0
return sign * funz, g, fail
opt_prob = Optimization('ODAA problem', objfun)
opt_prob.addObj('funz')
opt_prob.addCon('budget const', 'e')
opt_prob.addCon('VaR const', 'i')
slsqp = SLSQP() # instantiate Optimizer
slsqp.setOption('IPRINT', -1)
opt_prob.addVarGroup('u',
model.M,
'c',
lower=np.zeros(model.M),
upper=np.ones(model.M),
value=u0)
#print opt_prob # print optimization problem
slsqp(opt_prob,
sens_type='FD',
int_domain=int_domain,
J=J,
a=a,
model=model,
sign=sign)
#print opt_prob.solution(0) # print solution
return opt_prob
def __init__(self, nlpy_model, **kwargs):
"""
:parameters:
:nlpy_model: nonlinear problem from the NLPModel class or
from AmplModel class
"""
if nlpy_model.__module__ == 'nlpy.model.amplpy':
print 'AMPL model'
print "AMPL isn't handling complex values"
print "Choose 'FD' or 'opt_prob.grad_func' for sensitivity"
# Initialize model.
Optimization.__init__(self, nlpy_model.name, lambda x:
(self.nlpy_model.obj(x),
self.nlpy_model.cons(x).tolist(),0),
var_set={}, obj_set={}, con_set={},
use_groups=False, **kwargs)
self.nlpy_model = nlpy_model
self.addObj('f')
# Assigning lower and upper bounds on variables
for i in range(0,self.nlpy_model.n):
if i in self.nlpy_model.lowerB:
self.addVar("x"+ "%d"%(i+1), lower=self.nlpy_model.Lvar[i],
upper=numpy.inf, value=self.nlpy_model.x0[i])
elif i in self.nlpy_model.upperB:
self.addVar("x"+ "%d"%(i+1), lower=-numpy.inf,
upper=self.nlpy_model.Uvar[i], value=self.nlpy_model.x0[i])
elif i in self.nlpy_model.rangeB:
self.addVar("x"+ "%d"%(i+1), lower=self.nlpy_model.Lvar[i],
upper=self.nlpy_model.Uvar[i], value=self.nlpy_model.x0[i])
elif i in self.nlpy_model.freeB:
self.addVar("x"+ "%d"%(i+1), value=self.nlpy_model.x0[i],
lower=-numpy.inf, upper=numpy.inf)
# Assigning lower and upper bounds on constraints
for i in range(0,nlpy_model.m):
if i in nlpy_model.lowerC:
self.addCon("g"+"%d"%(i+1), 'i', lower=nlpy_model.Lcon[i],
upper=numpy.inf)
elif i in nlpy_model.upperC:
self.addCon("g"+"%d"%(i+1), 'i', lower=-numpy.inf,
upper=nlpy_model.Ucon[i])
elif i in nlpy_model.rangeC:
self.addCon("g"+"%d"%(i+1), 'i', lower=nlpy_model.Lcon[i],
upper=nlpy_model.Ucon[i])
elif i in nlpy_model.equalC:
self.addCon("g"+"%d"%(i+1), 'e', equal=nlpy_model.Lcon[i])
def translateProgramToPyOpt(dfovecProgram):
# Currently only handles inequality
def objfunc(x):
f = dfovecProgram.objective(x)
g = []
if dfovecProgram.hasInequalityConstraints():
g = dfovecProgram.inequalityConstraints(x)
fail = 0
return f, g, fail
opt_prob = Optimization('Dfovec problem', objfunc)
for i in range(len(dfovecProgram.x0)):
opt_prob.addVar('x' + str(i),
lower=-1000.0,
upper=1000.0,
value=dfovecProgram.x0[i])
opt_prob.addObj('f')
numIneq = dfovecProgram.getNumInequalityConstraints()
opt_prob.addConGroup('g',
numIneq,
type='i',
lower=[-10000] * numIneq,
upper=[0] * numIneq)
print(opt_prob)
return opt_prob
def otimiza(obitos, x0):
a, b, c, d = x0
partial_func = partial(problema, obitos=obitos)
update_wrapper(partial_func, problema)
# Instantiate Optimization Problem
opt_prob = Optimization('Rosenbrock Unconstraint Problem', partial_func)
# opt_prob.addVarGroup('x', 2, 'c', lower=-1e10, upper=0.5, value=-3.0)
# opt_prob.addVar('x1', 'c', lower=-10, upper=10, value=-3.0)
# opt_prob.addVar('x2', 'c', lower=-10, upper=10, value=-4.0)
opt_prob.addVar('A', 'c', lower=0, upper=2000, value=a)
opt_prob.addVar('B', 'c', lower=0, upper=2000, value=b)
opt_prob.addVar('C', 'c', lower=0, upper=2, value=c)
opt_prob.addVar('D', 'c', lower=0, upper=1, value=d)
# opt_prob.addCon('C', type='i', lower=0, upper=5, equal=c)
# opt_prob.addCon('D', type='i', lower=0, upper=1, equal=d)
opt_prob.addObj('f')
# print(opt_prob)
# from pyOpt.pySLSQP.pySLSQP import SLSQP
# sopt = SLSQP()
# sopt.setOption('IPRINT', -1)
from pyOpt.pySOLVOPT.pySOLVOPT import SOLVOPT
sopt = SOLVOPT()
sopt.setOption('iprint', -1)
[fstr, xstr, inform] = sopt(opt_prob, sens_type='FD')
solution = getlastsolution(opt_prob)
print(solution)
return xstr, solution
def solveOpt(int_domain, J, a, model, u0, sign):
'''
INPUT:
int_domain =
J =
a =
model =
u0 =
sign =
OUTPUT:
opt_prob =
'''
def objfun(u, **kwargs):
'''objfun defines optimization problem using the pyOpt sintax'''
# 1) extract paraeters
int_domain = kwargs['int_domain']
J = kwargs['J']
x = kwargs['a']
model = kwargs['model']
sign = kwargs['sign']
# 2) define objective function and constraints
funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x))
g = []
fail = 0
return sign * funz, g, fail
opt_prob = Optimization('ODAA problem', objfun)
opt_prob.addObj('funz')
solver = SLSQP() # choose the solver
solver.setOption('IPRINT', -1)
opt_prob.addVar('u', 'c', lower=-1, upper=1, value=u0)
#print opt_prob # print optimization problem
solver(opt_prob, int_domain=int_domain, J=J, a=a, model=model, sign=sign)
#print opt_prob.solution(0) # print solution
return opt_prob
def infill(self, points, method='error'):
## We'll be making non-permanent modifications to self.X and self.y here, so lets make a copy just in case
initX = np.copy(self.X)
inity = np.copy(self.y)
## This array will hold the new values we add
returnValues = np.zeros([points, self.k], dtype=float)
for i in range(points):
opt_prob1 = Optimization('InFillPSO',
self.errorObjective_normalized)
for k in range(self.k):
opt_prob1.addVar('{0}'.format(k),
'c',
lower=0,
upper=1,
value=.5)
pso1 = ALPSO()
pso1.setOption('SwarmSize', 100)
pso1.setOption('maxOuterIter', 100)
pso1.setOption('stopCriteria', 1)
pso1(opt_prob1)
newpoint = np.zeros(self.k)
for j in range(self.k):
newpoint[j] = opt_prob1.solution(0)._variables[j].value
returnValues[i][:] = self.inversenormX(newpoint)
self.addPoint(returnValues[i],
self.predict(returnValues[i]),
norm=True)
self.updateModel()
del (opt_prob1)
del (pso1)
self.X = np.copy(initX)
self.y = np.copy(inity)
self.n = len(self.X)
self.updateModel()
return returnValues
def configure(self, building):
# Get building and optimization setup properties
self.building = deepcopy(building)
self.T, self.states, self.actions, self.disturbances, self.controlLim, self.actionLim, self.comfort, self.occ, self.nvars, self.ncons = self.building.getConfiguration(
)
# Define Box Constraints (min/max values) for the control parameters
boxConstraints = []
for ii in range(self.nvars):
boxConstraints.append(self.controlLim)
# Link to the python function calculating the cost and the constraints
self.opt_prob = Optimization('SLSQP Constrained Problem',
self.wrapSimulation)
# Setupt Box Constrains in pyOpt
for ii in range(self.nvars):
self.opt_prob.addVar('x' + str(ii + 1),
'c',
lower=boxConstraints[ii][0],
upper=boxConstraints[ii][1],
value=self.building.policy[0, ii])
# Setupt Cost Function in pyOpt
self.opt_prob.addObj('f')
# Setupt Inequality Constraints in pyOpt
for ii in range(self.ncons):
self.opt_prob.addCon('g' + str(ii + 1), 'i')
# Print the Optimization setup
print("----------------------------------------")
print("----------------------------------------")
print("SLSQP Optimization setup:")
print(self.opt_prob)
def get_pyopt_optimization(f, g_f, con, g_con, x0, T):
opt_prob = Optimization('stoc planner', obj_fun(f, con))
opt_prob.addObj('f')
opt_prob.addVarGroup('flat_plan',
x0.size,
type='c',
value = x0,
lower = 0.,
upper = 1.0)
opt_prob.addConGroup('g', T, 'e')
# opt = SLSQP()
# opt = pySNOPT.SNOPT()
# opt = PSQP()
# opt = CONMIN()
opt = ALGENCAN()
return opt_prob, opt
def translateProgramToPyOpt(dfovecProgram):
# Currently only handles inequality
def objfunc(x):
f = dfovecProgram.objective(x)
g = []
if dfovecProgram.hasInequalityConstraints():
g = dfovecProgram.inequalityConstraints(x)
fail = 0
return f, g, fail
opt_prob = Optimization('Dfovec problem', objfunc)
for i in range(len(dfovecProgram.x0)):
opt_prob.addVar('x' + str(i), lower=-1000.0, upper=1000.0, value=dfovecProgram.x0[i])
opt_prob.addObj('f')
numIneq = dfovecProgram.getNumInequalityConstraints()
opt_prob.addConGroup('g', numIneq, type='i', lower=[-10000] * numIneq, upper=[0] * numIneq)
print(opt_prob)
return opt_prob
g_con[1][0] = x[1] * x[2] * x[3]
g_con[1][1] = x[0] * x[2] * x[3]
g_con[1][2] = x[0] * x[1] * x[3]
g_con[1][3] = x[0] * x[1] * x[2]
fail = 0
return g_obj, g_con, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('HS 071', objfunc)
opt_prob.addVar('x1', 'c', value=1., lower=1., upper=5.)
opt_prob.addVar('x2', 'c', value=5., lower=1., upper=5.)
opt_prob.addVar('x3', 'c', value=5., lower=1., upper=5.)
opt_prob.addVar('x4', 'c', value=1., lower=1., upper=5.)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'e', equal=40.)
opt_prob.addCon('g2', 'i', lower=25., upper=numpy.inf)
print opt_prob
# Instantiate Optimizer (IPOPT)
ipopt = IPOPT()
# Solve Problem with Optimizer Using Finite Differences
ipopt.setOption('output_file', 'ipopt.out')
#==============================================================================
# Start Matlab engine
#==============================================================================
eng = matlab.engine.start_matlab()
#Go to directory where matlab file is
eng.cd('..')
eng.cd('SMA_temperature_strain_driven')
# =============================================================================
#
# =============================================================================
chord = 1.
x_hinge = 0.75
safety = 0.005*chord
opt_prob = Optimization('Static model optimization',objfunc)
#xs_n
opt_prob.addVar('x1', 'c', lower = x_hinge/2. , upper = x_hinge - safety, value = 6.817445e-001)
#ys_n
opt_prob.addVar('x2', 'c', lower = -.9, upper = -0., value = -5.216475e-001)
#xs_p
opt_prob.addVar('x3', 'c', lower = x_hinge + safety, upper = chord - safety, value = 9.029895e-001)
#ys_p
opt_prob.addVar('x4', 'c', lower = 0., upper = .9, value = 8.726738e-001)
#xl_n
opt_prob.addVar('x5', 'c', lower = x_hinge/2., upper = x_hinge - safety, value = 6.958111e-001)
#yl_n
opt_prob.addVar('x6', 'c', lower = -.9, upper = 0.9, value = -4.593744e-001)
#xl_p
opt_prob.addVar('x7', 'c', lower = x_hinge + safety, upper = chord - safety, value = 8.187166e-001)
#yl_p
def main():
###########################################
# Define some values
###########################################
n_blades = 2
n_elements = 10
radius = unit_conversion.in2m(9.6) / 2
root_cutout = 0.1 * radius
dy = float(radius - root_cutout) / n_elements
dr = float(1) / n_elements
y = root_cutout + dy * np.arange(1, n_elements + 1)
r = y / radius
pitch = 0.0
airfoils = (('SDA1075_494p', 0.0, 1.0), )
allowable_Re = [
1000000., 500000., 250000., 100000., 90000., 80000., 70000., 60000.,
50000., 40000., 30000., 20000., 10000.
]
vehicle_weight = 12.455
max_chord = 0.3
max_chord_tip = 5.
alt = 0
tip_loss = True
mach_corr = False
# Forward flight parameters
v_inf = 4. # m/s
alpha0 = 0.0454 # Starting guess for trimmed alpha in radians
n_azi_elements = 5
# Mission times
time_in_hover = 300. # Time in seconds
time_in_ff = 500.
mission_time = [time_in_hover, time_in_ff]
Cl_tables = {}
Cd_tables = {}
Clmax = {}
# Get lookup tables
if any(airfoil[0] != 'simple' for airfoil in airfoils):
for airfoil in airfoils:
Cl_table, Cd_table, Clmax = aero_coeffs.create_Cl_Cd_table(
airfoil[0])
Cl_tables[airfoil[0]] = Cl_table
Cd_tables[airfoil[0]] = Cd_table
Clmax[airfoil[0]] = Clmax
# Create list of Cl functions. One for each Reynolds number. Cl_tables (and Cd_tables) will be empty for the
# 'simple' case, therefore this will be skipped for the simple case. For the full table lookup case this will be
# skipped because allowable_Re will be empty.
Cl_funs = {}
Cd_funs = {}
lift_curve_info_dict = {}
if Cl_tables and allowable_Re:
Cl_funs = dict(
zip(allowable_Re, [
aero_coeffs.get_Cl_fun(Re, Cl_tables[airfoils[0][0]],
Clmax[airfoils[0][0]][Re])
for Re in allowable_Re
]))
Cd_funs = dict(
zip(allowable_Re, [
aero_coeffs.get_Cd_fun(Re, Cd_tables[airfoils[0][0]])
for Re in allowable_Re
]))
lift_curve_info_dict = aero_coeffs.create_liftCurveInfoDict(
allowable_Re, Cl_tables[airfoils[0][0]])
###########################################
# Set design variable bounds
###########################################
# Hover opt 500 gen, 1000 pop, 12.455 N weight, 9.6 in prop
chord = np.array([
0.11923604, 0.2168746, 0.31540216, 0.39822882, 0.42919, 0.35039799,
0.3457828, 0.28567224, 0.23418368, 0.13502483
])
twist = np.array([
0.45316866, 0.38457724, 0.38225075, 0.34671967, 0.33151445, 0.28719111,
0.25679667, 0.25099005, 0.19400679, 0.10926302
])
omega = 3811.03596674 * 2 * np.pi / 60
original = (omega, chord, twist)
dtwist = np.array(
[twist[i + 1] - twist[i] for i in xrange(len(twist) - 1)])
dchord = np.array(
[chord[i + 1] - chord[i] for i in xrange(len(chord) - 1)])
twist0 = twist[0]
chord0 = chord[0]
omega_start = omega
dtwist_start = dtwist
dchord_start = dchord
twist0_start = twist0
chord0_start = chord0
omega_lower = 2000 * 2 * np.pi / 60
omega_upper = 8000.0 * 2 * np.pi / 60
twist0_lower = 0. * 2 * np.pi / 360
twist0_upper = 60. * 2 * np.pi / 360
chord0_upper = 0.1198
chord0_lower = 0.05
dtwist_lower = -10.0 * 2 * np.pi / 360
dtwist_upper = 10.0 * 2 * np.pi / 360
dchord_lower = -0.1
dchord_upper = 0.1
opt_prob = Optimization('Mission Simulator', objfun)
opt_prob.addVar('omega_h',
'c',
value=omega_start,
lower=omega_lower,
upper=omega_upper)
opt_prob.addVar('twist0',
'c',
value=twist0_start,
lower=twist0_lower,
upper=twist0_upper)
opt_prob.addVar('chord0',
'c',
value=chord0_start,
lower=chord0_lower,
upper=chord0_upper)
opt_prob.addVarGroup('dtwist',
n_elements - 1,
'c',
value=dtwist_start,
lower=dtwist_lower,
upper=dtwist_upper)
opt_prob.addVarGroup('dchord',
n_elements - 1,
'c',
value=dchord_start,
lower=dchord_lower,
upper=dchord_upper)
opt_prob.addObj('f')
opt_prob.addCon('thrust', 'i')
opt_prob.addCon('c_tip', 'i')
opt_prob.addConGroup('c_lower', n_elements, 'i')
opt_prob.addConGroup('c_upper', n_elements, 'i')
print opt_prob
slsqp = SLSQP()
slsqp.setOption('IPRINT', 1)
slsqp.setOption('MAXIT', 1000)
slsqp.setOption('ACC', 1e-8)
fstr, xstr, inform = slsqp(opt_prob,
sens_type='FD',
n_blades=n_blades,
radius=radius,
dy=dy,
dr=dr,
y=y,
r=r,
pitch=pitch,
airfoils=airfoils,
vehicle_weight=vehicle_weight,
max_chord=max_chord,
tip_loss=tip_loss,
mach_corr=mach_corr,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables,
allowable_Re=allowable_Re,
alt=alt,
v_inf=v_inf,
alpha0=alpha0,
mission_time=mission_time,
n_azi_elements=n_azi_elements,
lift_curve_info_dict=lift_curve_info_dict,
max_chord_tip=max_chord_tip)
print opt_prob.solution(0)
# pop_size = 300
# max_gen = 500
# opt_method = 'nograd'
# nsga2 = NSGA2()
# nsga2.setOption('PrintOut', 2)
# nsga2.setOption('PopSize', pop_size)
# nsga2.setOption('maxGen', max_gen)
# nsga2.setOption('pCross_real', 0.85)
# nsga2.setOption('xinit', 1)
# fstr, xstr, inform = nsga2(opt_prob, n_blades=n_blades, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch,
# airfoils=airfoils, vehicle_weight=vehicle_weight, max_chord=max_chord, tip_loss=tip_loss,
# mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables,
# Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method, alt=alt,
# v_inf=v_inf, alpha0=alpha0, mission_time=mission_time, n_azi_elements=n_azi_elements,
# pop_size=pop_size, max_gen=max_gen, lift_curve_info_dict=lift_curve_info_dict,
# max_chord_tip=max_chord_tip)
# print opt_prob.solution(0)
# opt_method = 'nograd'
# xstart_alpso = np.concatenate((np.array([omega_start, twist0_start, chord0_start]), dtwist_start, dchord_start))
# alpso = ALPSO()
# alpso.setOption('xinit', 0)
# alpso.setOption('SwarmSize', 200)
# alpso.setOption('maxOuterIter', 100)
# alpso.setOption('stopCriteria', 0)
# fstr, xstr, inform = alpso(opt_prob, xstart=xstart_alpso, n_blades=n_blades, n_elements=n_elements,
# root_cutout=root_cutout, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch,
# airfoils=airfoils, thrust=thrust, max_chord=max_chord, tip_loss=tip_loss,
# mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables,
# Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method)
# print opt_prob.solution(0)
def get_performance(o, c, t):
chord_meters = c * radius
prop = propeller.Propeller(t,
chord_meters,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
quad = quadrotor.Quadrotor(prop, vehicle_weight)
ff_kwargs = {
'propeller': prop,
'pitch': pitch,
'n_azi_elements': n_azi_elements,
'allowable_Re': allowable_Re,
'Cl_funs': Cl_funs,
'Cd_funs': Cd_funs,
'tip_loss': tip_loss,
'mach_corr': mach_corr,
'alt': alt,
'lift_curve_info_dict': lift_curve_info_dict
}
trim0 = np.array([alpha0, o])
alpha_trim, omega_trim, converged = trim.trim(quad, v_inf, trim0,
ff_kwargs)
T_ff, H_ff, P_ff = bemt.bemt_forward_flight(
quad,
pitch,
omega_trim,
alpha_trim,
v_inf,
n_azi_elements,
alt=alt,
tip_loss=tip_loss,
mach_corr=mach_corr,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
lift_curve_info_dict=lift_curve_info_dict)
dT_h, P_h = bemt.bemt_axial(prop,
pitch,
o,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
alt=alt)
return sum(dT_h), P_h, T_ff, P_ff, alpha_trim, omega_trim
omega = xstr[0]
twist0 = xstr[1]
chord0 = xstr[2]
dtwist = xstr[3:3 + len(r) - 1]
dchord = xstr[3 + len(r) - 1:]
twist = calc_twist_dist(twist0, dtwist)
chord = calc_chord_dist(chord0, dchord)
print "chord = " + repr(chord)
print "twist = " + repr(twist)
# twist_base = calc_twist_dist(twist0_base, dtwist_base)
# chord_base = calc_chord_dist(chord0_base, dchord_base)
perf_opt = get_performance(omega, chord, twist)
perf_orig = get_performance(original[0], original[1], original[2])
print "omega_orig = " + str(original[0])
print "Hover Thrust of original = " + str(perf_orig[0])
print "Hover Power of original = " + str(perf_orig[1])
print "FF Thrust of original = " + str(perf_orig[2])
print "FF Power of original = " + str(perf_orig[3])
print "Trim original (alpha, omega) = (%f, %f)" % (perf_orig[4],
perf_orig[5])
print "omega = " + str(omega * 60 / 2 / np.pi)
print "Hover Thrust of optimized = " + str(perf_opt[0])
print "Hover Power of optimized = " + str(perf_opt[1])
print "FF Thrust of optimized = " + str(perf_opt[2])
print "FF Power of optimized = " + str(perf_opt[3])
print "Trim optimized (alpha, omega) = (%f, %f)" % (perf_opt[4],
perf_opt[5])
# print "Omega base = " + str(omega_start*60/2/np.pi)
# print "Thrust of base = " + str(sum(perf_base[0]))
# print "Power of base = " + str(sum(perf_base[1]))
#
plt.figure(1)
plt.plot(r, original[1], '-b')
plt.plot(r, chord, '-r')
plt.xlabel('radial location')
plt.ylabel('c/R')
plt.legend(['start', 'opt'])
plt.figure(2)
plt.plot(r, original[2] * 180 / np.pi, '-b')
plt.plot(r, twist * 180 / np.pi, '-r')
plt.xlabel('radial location')
plt.ylabel('twist')
plt.legend(['start', 'opt'])
plt.show()
f = x0**2 + x1**2
g = [0.0]*2
g[0] = 3 - x0
g[1] = 2 - x1
fail = 0
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('TOY Constrained Problem',objfunc,use_groups=True)
opt_prob.addVarGroup('a',2,'c',value=1.0, lower=0.0, upper=10)
opt_prob.delVarGroup('a')
opt_prob.addVar('x','c',value=1.0, lower=0.0, upper=10)
opt_prob.addVarGroup('y',2,'c',value=1.0, lower=0.0, upper=10)
opt_prob.delVarGroup('y')
opt_prob.addVarGroup('z',1,'c',value=1.0, lower=0.0, upper=10)
opt_prob.addVarGroup('b',5,'c',value=3.0, lower=0.0, upper=10)
opt_prob.delVarGroup('b')
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
print(opt_prob)
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
def theor_variogram(experimental_sv, Sb=(0.01,400), Rb=(2,20), Nb=(0,400),
ab=(0,2), vb=(0,1000), candidate_sv=None,
candidate_sv_tag=None):
'''
Fitting of theoretical variogram
Parameters
----------
**experimental_sv** -- Experimental semivariogram ''[x,2]'', lag and \
semivariogram \n
**Sb** -- Boundaries on Sill of semivariogram ``(min,max)`` \n
**Rb** -- Boundaries on Range of semivariogram ``(min,max)`` \n
**Nb** -- Boundaries on Nugget of semivariogram ``(min,max)`` \n
**ab** -- Boundaries on Power of semivariogram ``(min,max)`` (only \
valid for power semivariogram) \n
**vb** -- Boundaries on Shape parameter of semivariogram ``(min,max)``\
(only valid for matérn type) \n
Returns
-------
**xopt** -- Vector with optimal semivariogram parameters ``[5]`` \n
**ModOpt** -- Pointer to optimal vector location \n
**candidate_sv** -- Array with pointer to functions in variogram_fit \
module
'''
if candidate_sv is None:
# Array with functions to be called from the Variograms library
candidate_sv = [variogram_fit.exponential_sv,
variogram_fit.gaussian_sv]
if candidate_sv_tag is None:
# Names of functions for display only
candidate_sv_tag = ['Exponential','Gaussian']
# Initial seed for variogram fit
sr = random.uniform(Sb[0], Sb[1])
rr = random.uniform(Rb[0], Rb[1])
nr = random.uniform(Nb[0], Nb[1])
ar = random.uniform(ab[0], ab[1])
vr = random.uniform(vb[0], vb[1])
Var = []
Res = []
Mdl = []
# Wrapper of minimisation function (RMSE) for semivariogram fitting
def _opt_fun(x,*args):
F, g, fail = variogram_fit.fit_function(x, experimental_sv,
j,candidate_sv)
if F == ERROR_CODE:
fail = 1
else:
Var.append(x)
Res.append(F)
Mdl.append(j)
return F, g, fail
# Optimisation starts to minimise differences between experimental and
# theoretical semivariograms
for j in xrange(0,len(candidate_sv)):
VarProb = Optimization('Variogram Fitting: ' + candidate_sv_tag[j],
_opt_fun)
VarProb.addObj('RMSE')
VarProb.addVar('Sill', 'c', lower=Sb[0], upper=Sb[1], value=sr)
VarProb.addVar('Range', 'c', lower=Rb[0], upper=Rb[1], value=rr)
VarProb.addVar('Nugget', 'c', lower=Nb[0], upper=Nb[1], value=nr)
VarProb.addVar('Exponent (a)', 'c', lower=ab[0], upper=ab[1], value=ar)
VarProb.addVar('Rank (v)', 'c', lower=vb[0], upper=vb[1], value=vr)
args = (experimental_sv, j, candidate_sv, Var, Res, Mdl)
optmz = ALHSO()
optmz.setOption('fileout',0)
optmz(VarProb)
# Get pointer to best semivariogram
k = np.argmin(Res)
xopt = Var[k]
ModOpt = Mdl[k]
return xopt, ModOpt, candidate_sv
def getlastsolution(prob: Optimization):
new_index = prob.firstavailableindex(prob.getSolSet())
return prob.getSol(new_index - 1)
f = -x[0] * x[1] * x[2]
g = [0.0] * 2
g[0] = x[0] + 2. * x[1] + 2. * x[2] - 72.0
g[1] = -x[0] - 2. * x[1] - 2. * x[2]
time.sleep(0.5)
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('TP37 Constrained Problem', objfunc)
opt_prob.addVar('x1', 'c', lower=0.0, upper=42.0, value=10.0)
opt_prob.addVar('x2', 'c', lower=0.0, upper=42.0, value=10.0)
opt_prob.addVar('x3', 'c', lower=0.0, upper=42.0, value=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
opt_prob.addCon('g2', 'i')
# Instantiate Optimizer (SLSQP)
slsqp = SLSQP()
slsqp.setOption('IPRINT', -1)
# Solve Problem (Without Parallel Gradient)
slsqp(opt_prob, sens_type='CS')
# end
g=[]
fail=1
os.chdir('../..')
CASECOUNT+=1
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Minimize Drag',objfunc)
opt_prob.addVar('x1','c',lower=1.,upper=25.,value=12.0)
opt_prob.addVar('x2','c',lower=1.,upper=30.,value=15.0)
opt_prob.addVar('x3','c',lower=1.,upper=30.,value=15.0)
opt_prob.addVar('x4','c',lower=1.,upper=25.,value=12.0)
opt_prob.addVar('x5','c',lower=1.,upper=25.,value=12.0)
opt_prob.addVar('x6','c',lower=1.,upper=13.,value=6.0)
opt_prob.addObj('f')
#opt_prob.addCon('g','i')
print opt_prob
mkdirCommand='mkdir -p workDir'
subprocess.call(mkdirCommand,shell=True)
# Instantiate Optimizer (NSGA2) & Solve Problem
nsga2 = NSGA2()
def variogram_fit(SVExp,
Sb=(0.01, 400),
Rb=(2, 20),
Nb=(0, 400),
ab=(0, 2),
vb=(0, 1000)):
# Array with functions to be called from the Variograms library
VarFunArr = [
VariogramFit.SVExponential, VariogramFit.SVGaussian,
VariogramFit.SVSpherical, VariogramFit.SVCubic,
VariogramFit.SVPentaspherical, VariogramFit.SVSinehole,
VariogramFit.SVPower, VariogramFit.SVMatern
]
# Names of functions for display only
optFunNam = [
'Exponential', 'Gaussian', 'Spherical', 'Cubic', 'Pentaspherical',
'Sinehole', 'Power', 'Matern'
]
# Boundaries semivariogram parameters
#Sb = (0.01,400) # Limit for the sill
#Rb = (2,20) # Limit for the range
#Nb = (0,400) # Limit for the Nugget effect
#ab = (0,2) # Limit for a in power variogram
#vb = (0,1000) # Limit for Matern v parameters
# Initial seed for variogram fit
sr = random.uniform(Sb[0], Sb[1])
rr = random.uniform(Rb[0], Rb[1])
nr = random.uniform(Nb[0], Nb[1])
ar = random.uniform(ab[0], ab[1])
vr = random.uniform(vb[0], vb[1])
return sr, rr, nr, ar, vr
Var = []
Res = []
Mdl = []
# Wrapper of minimisation function (RMSE) for semivariogram fitting
def OptFun(x, *args):
F, g, fail = VariogramFit.optFunMaster(x, SVExp, j, VarFunArr)
if F == 9999:
fail = 1
else:
Var.append(x)
Res.append(F)
Mdl.append(j)
return F, g, fail
print 'Initialising Variogram fit'
print ''
# Optimisation starts to minimise differences between experimental and
# theoretical semivariograms
for j in xrange(0, len(VarFunArr)):
print 'Variogram Fitting ' + optFunNam[j]
print ''
VarProb = Optimization('Variogram Fitting: ' + optFunNam[j], OptFun)
VarProb.addObj('RMSE')
VarProb.addVar('Sill', 'c', lower=Sb[0], upper=Sb[1], value=sr)
VarProb.addVar('Range', 'c', lower=Rb[0], upper=Rb[1], value=rr)
VarProb.addVar('Nugget', 'c', lower=Nb[0], upper=Nb[1], value=nr)
VarProb.addVar('Exponent (a)', 'c', lower=ab[0], upper=ab[1], value=ar)
VarProb.addVar('Rank (v)', 'c', lower=vb[0], upper=vb[1], value=vr)
args = (SVExp, j, VarFunArr, Var, Res, Mdl)
optmz = ALHSO()
optmz(VarProb)
print VarProb.solution(0)
print ''
# Get position of best semivariogram
k = numpy.argmin(Res)
xopt = Var[k]
ModOpt = Mdl[k]
del Var
del Res
del Mdl
print 'Theoretical variogram fit - Done!'
print ''
return xopt, ModOpt, VarFunArr
g_con[2][0] = 4.*x[0] + 2
g_con[2][1] = 2.*x[1] - 1
g_con[2][2] = 2.*x[2]
g_con[2][3] = -1.
fail = 0
return g_obj,g_con,fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Constrained Rosen-Suzuki',objfunc)
opt_prob.addVar('x1','c',value=1.5)
opt_prob.addVar('x2','c',value=1.5)
opt_prob.addVar('x3','c',value=1.5)
opt_prob.addVar('x4','c',value=1.5)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
opt_prob.addCon('g3','i')
print(opt_prob)
# Instantiate Optimizer (CONMIN)
conmin = CONMIN()
# Solve Problem with Optimizer Using Finite Differences
conmin(opt_prob,sens_type='FD')
#==============================================================================
# Start Matlab engine
#==============================================================================
eng = matlab.engine.start_matlab()
#Go to directory where matlab file is
eng.cd('..')
eng.cd('SMA_temperature_strain_driven')
# =============================================================================
#
# =============================================================================
chord = 1.
x_hinge = 0.75
safety = 0.005*chord
opt_prob = Optimization('Static model optimization', objfunc)
#l_s
opt_prob.addVar('x1', 'c', lower = 0.1 , upper = 0.6, value = 0.2)
#l_l
opt_prob.addVar('x2', 'c', lower = 0.1, upper = 0.6, value = 0.2)
#R
opt_prob.addVar('x5', 'c', lower = 0.001, upper = 0.03, value = 0.02)
# #yl_n
# opt_prob.addVar('x6', 'c', lower = -.9, upper = 0.9, value = -4.593744e-001)
# #xl_p
# opt_prob.addVar('x7', 'c', lower = x_hinge + safety, upper = chord - safety, value = 8.187166e-001)
# #yl_p
# opt_prob.addVar('x8', 'c', lower = -.9, upper = 0., value = -5.719241e-001)
opt_prob.addObj('f')
#opt_prob.addCon('g', 'i')
def variogram_fit(SVExp, Sb=(0.01,400), Rb=(2,20), Nb=(0,400),
ab=(0,2), vb=(0,1000)):
# Array with functions to be called from the Variograms library
VarFunArr = [VariogramFit.SVExponential, VariogramFit.SVGaussian,
VariogramFit.SVSpherical, VariogramFit.SVCubic,
VariogramFit.SVPentaspherical, VariogramFit.SVSinehole,
VariogramFit.SVPower, VariogramFit.SVMatern]
# Names of functions for display only
optFunNam = ['Exponential','Gaussian','Spherical','Cubic',
'Pentaspherical','Sinehole','Power','Matern']
# Boundaries semivariogram parameters
#Sb = (0.01,400) # Limit for the sill
#Rb = (2,20) # Limit for the range
#Nb = (0,400) # Limit for the Nugget effect
#ab = (0,2) # Limit for a in power variogram
#vb = (0,1000) # Limit for Matern v parameters
# Initial seed for variogram fit
sr = random.uniform(Sb[0],Sb[1])
rr = random.uniform(Rb[0],Rb[1])
nr = random.uniform(Nb[0],Nb[1])
ar = random.uniform(ab[0],ab[1])
vr = random.uniform(vb[0],vb[1])
return sr, rr, nr, ar, vr
Var = []
Res = []
Mdl = []
# Wrapper of minimisation function (RMSE) for semivariogram fitting
def OptFun(x,*args):
F, g, fail = VariogramFit.optFunMaster(x,SVExp,j,VarFunArr)
if F == 9999:
fail = 1
else:
Var.append(x)
Res.append(F)
Mdl.append(j)
return F, g, fail
print 'Initialising Variogram fit'
print ''
# Optimisation starts to minimise differences between experimental and
# theoretical semivariograms
for j in xrange(0,len(VarFunArr)):
print 'Variogram Fitting ' + optFunNam[j]
print ''
VarProb = Optimization('Variogram Fitting: ' + optFunNam[j], OptFun)
VarProb.addObj('RMSE')
VarProb.addVar('Sill','c',lower=Sb[0],upper=Sb[1],value=sr)
VarProb.addVar('Range','c',lower=Rb[0],upper=Rb[1],value=rr)
VarProb.addVar('Nugget','c',lower=Nb[0],upper=Nb[1],value=nr)
VarProb.addVar('Exponent (a)','c',lower=ab[0],upper=ab[1],value=ar)
VarProb.addVar('Rank (v)','c',lower=vb[0],upper=vb[1],value=vr)
args = (SVExp, j, VarFunArr, Var, Res, Mdl)
optmz = ALHSO()
optmz(VarProb)
print VarProb.solution(0)
print ''
# Get position of best semivariogram
k = numpy.argmin(Res)
xopt = Var[k]
ModOpt = Mdl[k]
del Var
del Res
del Mdl
print 'Theoretical variogram fit - Done!'
print ''
return xopt, ModOpt, VarFunArr
#==============================================================================
# Start Matlab engine
#==============================================================================
eng = matlab.engine.start_matlab()
#Go to directory where matlab file is
eng.cd('..')
eng.cd('SMA_temperature_strain_driven')
# =============================================================================
#
# =============================================================================
chord = 1.
x_hinge = 0.75
safety = 0.05*chord
opt_prob = Optimization('Static model optimization',objfunc)
#xs_n
opt_prob.addVar('x1', 'c', lower = x_hinge/2. , upper = x_hinge - safety, value = 7.407724e-001)
#ys_n
opt_prob.addVar('x2', 'c', lower = -.9, upper = -0., value = -3.680615e-001)
#xs_p
opt_prob.addVar('x3', 'c', lower = x_hinge + safety, upper = chord - safety, value = 9.933211e-001)
#ys_p
opt_prob.addVar('x4', 'c', lower = 0., upper = .9, value = 6.004423e-001)
#xl_n
opt_prob.addVar('x5', 'c', lower = x_hinge/2., upper = x_hinge - safety, value = 7.290939e-001)
#yl_n
opt_prob.addVar('x6', 'c', lower = -.9, upper = 0.9, value = -7.584186e-001)
#xl_p
opt_prob.addVar('x7', 'c', lower = x_hinge + safety, upper = chord - safety, value = 7.550874e-001)
#yl_p
def objfunc(xdict):
x = xdict['x']
y = xdict['y']
ff = [
(x - 0.0)**2 + (y - 0.0)**2,
(x - 1.0)**2 + (y - 1.0)**2,
]
gg = []
fail = False
return ff, gg, fail
# Instantiate Optimization Problem
optProb = Optimization('Rosenbrock function', objfunc, use_groups=True)
optProb.addVar('x', 'c', value=0, lower=-600, upper=600)
optProb.addVar('y', 'c', value=0, lower=-600, upper=600)
optProb.addObj('obj1')
optProb.addObj('obj2')
# 300 generations will find x=(0,0), 200 or less will find x=(1,1)
options = {
'maxGen': 200,
}
opt = NSGA2(options=options)
opt.setOption('PrintOut', 0)
opt(optProb)
print(optProb.getSol(0))
g[0] = x[0] - 1.0
g[1] = 1.333333333 - x[1]
g[2] = 2.666666666 - x[2]
time.sleep(0.005)
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('MIDACO Toy Problem', objfunc)
opt_prob.addVar('x1', 'c', lower=1.0, upper=4.0, value=1.0)
opt_prob.addVar('x2', 'c', lower=1.0, upper=4.0, value=1.0)
opt_prob.addVar('x3', 'c', lower=1.0, upper=4.0, value=1.0)
opt_prob.addVar('x4', 'c', lower=1.0, upper=4.0, value=1.0)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'e')
opt_prob.addCon('g2', 'i')
opt_prob.addCon('g3', 'i')
# Solve Problem (No-Parallelization)
midaco_none = MIDACO()
midaco_none.setOption('IPRINT', -1)
midaco_none.setOption('MAXEVAL', 50000)
midaco_none(opt_prob)
if myrank == 0:
f = x[0]**2 + x[1]**2
g = [0.0]*2
g[0] = 3 - x[0]
g[1] = 2 - x[1]
fail = 0
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instanciate Optimization Problem
opt_prob = Optimization('TOY Constraint Problem',objfunc)
opt_prob.addVar('x1','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addVar('x2','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
print opt_prob
# Instanciate Optimizer (ALPSO) & Solve Problem Storing History
slsqp = SLSQP()
slsqp.setOption('IFILE','slsqp1.out')
slsqp(opt_prob,store_hst=True)
print opt_prob.solution(0)
# Solve Problem Using Stored History (Warm Start)
slsqp.setOption('IFILE','slsqp2.out')
# =============================================================================
#
# =============================================================================
def objfunc(x):
f = 100*(x[1]-x[0]**2)**2+(1-x[0])**2
g = []
fail = 0
return f,g, fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('Rosenbrock Unconstraint Problem',objfunc)
opt_prob.addVar('x1','c',lower=-10.0,upper=10.0,value=-3.0)
opt_prob.addVar('x2','c',lower=-10.0,upper=10.0,value=-4.0)
opt_prob.addObj('f')
print opt_prob
# Instantiate Optimizer (PSQP) & Solve Problem
psqp = PSQP()
psqp.setOption('IPRINT',0)
psqp(opt_prob,sens_type='FD')
print opt_prob.solution(0)
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
slsqp.setOption('IPRINT',-1)
slsqp(opt_prob,sens_type='FD')
g = [0.0]*2
g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0
g[1] = -x[0] - 2.*x[1] - 2.*x[2]
time.sleep(0.5)
fail = 0
return f,g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('TP37 Constrained Problem',objfunc)
opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
# Solve Problem (No-Parallelization)
nlpqlp_none = NLPQLP()
nlpqlp_none.setOption('IPRINT',0)
nlpqlp_none(opt_prob)
if myrank == 0:
print(opt_prob.solution(0))
#end
def solveOpt(int_domain,J,x,model,u0):
def objfun(u,**kwargs):
# 1) extract paraeters
int_domain = kwargs['int_domain']
J = kwargs['J']
x = kwargs['x']
model = kwargs['model']
# 2) define objective function
f = np.trapz(int_domain,J * model.pf(int_domain,u,x))
g = [0]*2
# 3) budget constraint
g[1] = u.sum() - 1
# 4) VaR constarint
W = model.W
sigmaMax = model.VaR / norm.ppf(1-model.alpha)
g[0] = -sigmaMax + np.sqrt(W.dot(u).dot(u))
fail = 0
return f,g,fail
opt_prob = Optimization('test problem',objfun)
opt_prob.addObj('f')
opt_prob.addCon('budget const','e')
opt_prob.addCon('VaR const','i')
opt_prob.addVarGroup('u',model.M,'c',lower=np.zeros(model.M),
upper=np.ones(model.M),value=u0)
print opt_prob
slsqp = SLSQP()
slsqp.setOption('IPRINT',-1)
slsqp(opt_prob,sens_type='FD',int_domain=int_domain,J=J,x=x,model=model)
print opt_prob.solution(0)
# =============================================================================
#
# =============================================================================
def objfunc(x):
f = 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2
g = []
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('Rosenbrock Unconstraint Problem', objfunc)
opt_prob.addVar('x1', 'c', lower=-10.0, upper=10.0, value=-3.0)
opt_prob.addVar('x2', 'c', lower=-10.0, upper=10.0, value=-4.0)
opt_prob.addObj('f')
print(opt_prob)
# Instantiate Optimizer (PSQP) & Solve Problem
psqp = PSQP()
psqp.setOption('IPRINT', 0)
psqp(opt_prob, sens_type='FD')
print(opt_prob.solution(0))
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
slsqp.setOption('IPRINT', -1)
slsqp(opt_prob, sens_type='FD')
dareadx = 0.5 * (h1 + h2) * dedx + 0.5 * e * (dh1dx + dh2dx)
return dareadx
################################################################################
dp = crm_togw()
design_problem = PyOptOptimization(dp.comm,
dp.eval_objcon,
dp.eval_objcon_grad,
number_of_steps=3)
opt_prob = Optimization('crm_togw', design_problem.eval_obj_con)
opt_prob.addObj('TOGW')
opt_prob.addCon('cruise_lift', type='e')
opt_prob.addCon('maneuver_lift', type='e')
opt_prob.addCon('area', type='e')
opt_prob.addCon('ksfailure', type='i')
for i in range(187 - 3):
opt_prob.addCon('Smoothness %i a' % i, type='i')
opt_prob.addCon('Smoothness %i b' % i, type='i')
variables = dp.model.get_variables()
for i, var in enumerate(variables):
print('i', i)
f = x[0]**2 + x[1]**2
g = [0.0]*2
g[0] = 3 - x[0]
g[1] = 2 - x[1]
fail = 0
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instanciate Optimization Problem
opt_prob = Optimization('TOY Constrained Problem',objfunc)
opt_prob.addVar('x1','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addVar('x2','c',value=1.0,lower=0.0,upper=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
print(opt_prob)
# Instanciate Optimizer (SLSQP) & Solve Problem Storing History
slsqp = SLSQP()
slsqp.setOption('IFILE','slsqp1.out')
slsqp(opt_prob,store_hst=True)
print(opt_prob.solution(0))
# Solve Problem Using Stored History (Warm Start)
slsqp.setOption('IFILE','slsqp2.out')
from pyOpt import SOLVOPT
from pyOpt import KSOPT
from pyOpt import NSGA2
from pyOpt import SDPEN
def objfun(x):
f = 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2
g = []
fail = 0
return f, g, fail
opt_prob = Optimization('Rosenbrock Unconstrained Problem', objfun)
opt_prob.addVar('x1', 'c', lower=-10.0, upper=10.0, value=0.0)
opt_prob.addVar('x2', 'c', lower=-10.0, upper=10.0, value=0.0)
opt_prob.addObj('f')
print opt_prob
# Instantiate optimizer (PSQP) and solve problem
psqp = PSQP()
psqp.setOption('IPRINT', 0)
psqp(opt_prob, sens_type='FD')
print opt_prob.solution(0)
# Instantiate optimizer (SLSQP) and solve problem
slsqp = SLSQP()
slsqp.setOption('IPRINT', -1)
slsqp(opt_prob, sens_type='FD')
g_con[2][0] = 4. * x[0] + 2
g_con[2][1] = 2. * x[1] - 1
g_con[2][2] = 2. * x[2]
g_con[2][3] = -1.
fail = 0
return g_obj, g_con, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Constrained Rosen-Suzuki', objfunc)
opt_prob.addVar('x1', 'c', value=1.5)
opt_prob.addVar('x2', 'c', value=1.5)
opt_prob.addVar('x3', 'c', value=1.5)
opt_prob.addVar('x4', 'c', value=1.5)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
opt_prob.addCon('g2', 'i')
opt_prob.addCon('g3', 'i')
print opt_prob
# Instantiate Optimizer (CONMIN)
conmin = CONMIN()
# Solve Problem with Optimizer Using Finite Differences
conmin(opt_prob, sens_type='FD')
#print 'i: %d' % i
g[g_index] = num - divisor * mult_const * float(
CONS_RHS_MAX_2_php[j - 1])
num = 0
for i in range(len(DM_php)):
num = num + DM_php[i] * float(NUTRIENTS_php[j - 2][i]) * x[i]
g[g_index +
1] = -(num - divisor * mult_const * float(CONS_LHS_MIN_2_php[j - 1]))
#time.sleep(0.5)
fail = 0
return f, g, fail
opt_prob = Optimization('TP37 Constrained Problem', objfunc)
for i in range(1, len(CONS_LHS_MIN_1_php) + 1):
x_value = 'x' + str(i)
opt_prob.addVar(x_value,
'c',
lower=float(CONS_LHS_MIN_1_php[i - 1]),
upper=float(CONS_RHS_MAX_1_php[i - 1]),
value=1)
opt_prob.addObj('f')
for i in range(1, len(NUTRIENTS_php) * 2 + 1):
g_value = 'g' + str(i)
opt_prob.addCon(g_value, 'i')
# Instantiate Optimizer (SLSQP)
f = -x[0]*x[1]*x[2]
g = [0.0]*2
g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0
g[1] = -x[0] - 2.*x[1] - 2.*x[2]
fail = 0
return f,g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('TP37 Constrained Problem',objfunc)
opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
opt_prob.addCon('g2','i')
print opt_prob
# Instantiate Optimizer (PSQP) & Solve Problem
psqp = PSQP()
psqp.setOption('IPRINT',0)
psqp(opt_prob,sens_type='FD')
print opt_prob.solution(0)
# Instantiate Optimizer (SLSQP) & Solve Problem
for i in xrange(5):
f += -(c[i]*exp(-(1/pi)*((x[0]-a[i])**2 + (x[1]-b[i])**2))*cos(pi*((x[0]-a[i])**2 + (x[1]-b[i])**2)))
#end
g = [0.0]*1
g[0] = 20.04895 - (x[0]+2.0)**2 - (x[1]+1.0)**2
fail = 0
return f,g,fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('Langermann Function 11',objfunc)
opt_prob.addVar('x1','c',lower=-2.0,upper=10.0,value=8.0)
opt_prob.addVar('x2','c',lower=-2.0,upper=10.0,value=8.0)
opt_prob.addObj('f')
opt_prob.addCon('g','i')
print opt_prob
# Global Optimization
nsga2 = NSGA2()
nsga2(opt_prob)
print opt_prob.solution(0)
# Local Optimization Refinement
slsqp = SLSQP()
slsqp(opt_prob.solution(0))
print opt_prob.solution(0).solution(0)
g=[]
fail=1
os.chdir('../..')
CASECOUNT+=1
return f,g,fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Minimize Drag',objfunc)
opt_prob.addVar('x1','c',lower=1.,upper=25.,value=12.0)
opt_prob.addVar('x2','c',lower=1.,upper=30.,value=15.0)
opt_prob.addVar('x3','c',lower=1.,upper=30.,value=15.0)
opt_prob.addVar('x4','c',lower=1.,upper=25.,value=12.0)
opt_prob.addVar('x5','c',lower=1.,upper=25.,value=12.0)
opt_prob.addVar('x6','c',lower=1.,upper=13.,value=6.0)
opt_prob.addObj('f')
#opt_prob.addCon('g','i')
print opt_prob
mkdirCommand='mkdir -p workDir'
subprocess.call(mkdirCommand,shell=True)
# Instantiate Optimizer (NSGA2) & Solve Problem
nsga2 = NSGA2()
a3 = kwargs['a3']
f = a1*(x[1]-x[0]**2.)**2. + (a2-x[0])**2.
g = [0.0]*2
g[0] = x[0]**2. + x[1]**2.0 - a3
fail = 0
return f,g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Rosenbrock Constrained Problem',objfunc)
opt_prob.addVar('x1','c',lower=0.0,upper=1.0,value=0.5)
opt_prob.addVar('x2','c',lower=0.0,upper=1.0,value=0.5)
opt_prob.addObj('f')
opt_prob.addCon('g1','i')
print(opt_prob)
# Arguments to pass into objfunc
a1 = 100.0
a2 = 1.0
a3 = 1.0
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
slsqp.setOption('IPRINT',-1)
slsqp(opt_prob,sens_type='FD',a12=[a1,a2],a3=a3)
a3 = kwargs['a3']
f = a1 * (x[1] - x[0]**2.)**2. + (a2 - x[0])**2.
g = [0.0] * 2
g[0] = x[0]**2. + x[1]**2.0 - a3
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('Rosenbrock Constrained Problem', objfunc)
opt_prob.addVar('x1', 'c', lower=0.0, upper=1.0, value=0.5)
opt_prob.addVar('x2', 'c', lower=0.0, upper=1.0, value=0.5)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
print opt_prob
# Arguments to pass into objfunc
a1 = 100.0
a2 = 1.0
a3 = 1.0
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
slsqp.setOption('IPRINT', -1)
slsqp(opt_prob, sens_type='FD', a12=[a1, a2], a3=a3)
def runoptimizer():
opt_prob = Optimization('TP37 Constrained Problem',objfun)
opt_prob.addObj('LL')
opt_prob.addVar('x1','c',lower=0.01,upper=10.0,value=1.0)
opt_prob.addVar('x2','c',lower=0.01,upper=10.0,value=1.0)
opt_prob.addVar('x3','c',lower=0.01,upper=10.0,value=1.0)
opt_prob.addVar('x4','c',lower=0.01,upper=10.0,value=1.0)
opt_prob.addConGroup('g', 4, 'i')
# sanity check
print opt_prob
print objfun([1.0,1.0,1.0,1.0])
# other optimization methods can be used here - we use sequential least squares programming
slsqp = SLSQP()
[fstr, xstr, inform] = slsqp(opt_prob)
print opt_prob.solution(0)
return [v.value for v in opt_prob.solution(0).getVarSet().values()]
maxiter= comm.bcast(maxiter, root=0)
zvariable = comm.bcast(zvariable, root=0)
mymodelstruct = comm.bcast(mymodelstruct, root=0)
isdrude = comm.bcast(isdrude, root=0)
n = comm.bcast(n, root=0)
myinputdata = comm.bcast(myinputdata, root=0)
z = comm.bcast(z, root=0)
pathwithoutsample = comm.bcast(pathwithoutsample, root=0)
pathwithsample = comm.bcast(pathwithsample, root=0)
except:
print("No parallelization")
## Optimization dans le cas PyOpt swarm particle ALPSO without parallelization (also works with parallelization)
if algo>1:
interm2=0 ## Intermediate variable with a function similar to interm
opt_prob = Optimization('Dielectric modeling based on TDS pulse fitting',objfunc)
if zvariable==1:
opt_prob.addVar('thickness','c',lower=lb[0],upper=up[0],value=drudeinput[0])
interm2=interm2+1
if mymodelstruct==2: #in case of TDCMT
opt_prob.addVar('w0 tdcmt','c',lower=lb[0+interm2],upper=up[0+interm2],value=drudeinput[0+interm2])
opt_prob.addVar('tau0','c',lower=lb[1+interm2],upper=up[1+interm2],value=drudeinput[1+interm2])
opt_prob.addVar('tau1','c',lower=lb[2+interm2],upper=up[2+interm2],value=drudeinput[2+interm2])
opt_prob.addVar('tau2','c',lower=lb[3+interm2],upper=up[3+interm2],value=drudeinput[3+interm2])
opt_prob.addVar('delta theta','c',lower=lb[4+interm2],upper=up[4+interm2],value=drudeinput[4+interm2])
interm2=interm2+5
opt_prob.addVar('eps inf','c',lower=lb[0+interm2],upper=up[0+interm2],value=drudeinput[0+interm2])
if isdrude==1:
opt_prob.addVar('omega p','c',lower=lb[1+interm2],upper=up[1+interm2],value=drudeinput[1+interm2])
def train(self, optimizer='pso'):
#Define the optimization problem for training the kriging model
opt_prob = Optimization('Surrogate Test', self.fittingObjective)
for i in range(self.k):
opt_prob.addVar('theta%d' % i,
'c',
lower=1e-3,
upper=1e2,
value=.1)
for i in range(self.k):
opt_prob.addVar('pl%d' % i, 'c', lower=1.5, upper=2, value=2)
opt_prob.addVar('lambda', 'c', lower=1e-5, upper=1, value=1)
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
if optimizer == 'pso':
optimizer = ALPSO()
optimizer.setOption('SwarmSize', 150)
optimizer.setOption('maxOuterIter', 150)
optimizer.setOption('stopCriteria', 1)
optimizer.setOption('filename', '{0}Results.log'.format(self.name))
if optimizer == 'ga':
optimizer = NSGA2()
optimizer.setOption('PopSize', (4 * 50))
while True:
try:
self.trainingOptimizer(optimizer, opt_prob)
except Exception as e:
print e
print 'Error traning Model, restarting the optimizer with a larger population'
if optimizer == 'ga':
optimizer.setOption('SwarmSize', 200)
optimizer.setOption('maxOuterIter', 100)
optimizer.setOption('stopCriteria', 1)
if optimizer == 'ga':
optimizer.setOption('PopSize', 400)
else:
break
g_con[2][0] = 4.0 * x[0] + 2
g_con[2][1] = 2.0 * x[1] - 1
g_con[2][2] = 2.0 * x[2]
g_con[2][3] = -1.0
fail = 0
return g_obj, g_con, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization("Constrained Rosen-Suzuki", objfunc)
opt_prob.addVar("x1", "c", value=1.5)
opt_prob.addVar("x2", "c", value=1.5)
opt_prob.addVar("x3", "c", value=1.5)
opt_prob.addVar("x4", "c", value=1.5)
opt_prob.addObj("f")
opt_prob.addCon("g1", "i")
opt_prob.addCon("g2", "i")
opt_prob.addCon("g3", "i")
print opt_prob
# Instantiate Optimizer (CONMIN)
conmin = CONMIN()
# Solve Problem with Optimizer Using Finite Differences
conmin(opt_prob, sens_type="FD")
def execute(self):
"""pyOpt execution. Note that pyOpt controls the execution, and the
individual optimizers control the iteration."""
self.pyOpt_solution = None
opt_prob = Optimization(self.title, self.objfunc, var_set={}, obj_set={}, con_set={})
# Add all parameters
self.param_type = {}
for name, param in self.get_parameters().iteritems():
# We need to identify Enums, Lists, Dicts
metadata = param.get_metadata()[0][1]
values = param.evaluate()
# Assuming uniform enumerated, discrete, or continuous for now.
val = values[0]
choices = []
if "values" in metadata and isinstance(metadata["values"], (list, tuple, array, set)):
vartype = "d"
choices = metadata["values"]
elif isinstance(val, bool):
vartype = "d"
choices = [True, False]
elif isinstance(val, (int, int32, int64)):
vartype = "i"
elif isinstance(val, (float, float32, float64)):
vartype = "c"
else:
msg = "Only continuous, discrete, or enumerated variables" " are supported. %s is %s." % (
name,
type(val),
)
self.raise_exception(msg, ValueError)
self.param_type[name] = vartype
names = param.names
lower_bounds = param.get_low()
upper_bounds = param.get_high()
for i in range(param.size):
opt_prob.addVar(
names[i], vartype, lower=lower_bounds[i], upper=upper_bounds[i], value=values[i], choices=choices
)
# Add all objectives
for name in self.get_objectives():
opt_prob.addObj(name)
# Add all equality constraints
for name in self.get_eq_constraints():
opt_prob.addCon(name, type="e")
# Add all inequality constraints
for name in self.get_ineq_constraints():
opt_prob.addCon(name, type="i")
# Instantiate the requested optimizer
optimizer = self.optimizer
try:
exec ("from pyOpt import %s" % optimizer)
except ImportError:
msg = "Optimizer %s is not available in this installation." % optimizer
self.raise_exception(msg, ImportError)
optname = vars()[optimizer]
opt = optname()
# Set optimization options
for option, value in self.options.iteritems():
opt.setOption(option, value)
# Execute the optimization problem
if self.pyopt_diff:
# Use pyOpt's internal finite difference
opt(opt_prob, sens_type="FD")
else:
# Use OpenMDAO's differentiator for the gradient
opt(opt_prob, sens_type=self.gradfunc)
# Print results
if self.print_results:
print opt_prob.solution(0)
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dvals = []
for i in range(0, len(opt_prob.solution(0)._variables)):
dvals.append(opt_prob.solution(0)._variables[i].value)
# Integer parameters come back as floats, so we need to round them
# and turn them into python integers before setting.
if "i" in self.param_type.values():
for j, param in enumerate(self.get_parameters().keys()):
if self.param_type[param] == "i":
dvals[j] = int(round(dvals[j]))
self.set_parameters(dvals)
self.run_iteration()
self.record_case()
# Save the most recent solution.
self.pyOpt_solution = opt_prob.solution(0)
(x[1] - b[i])**2)) * cos(pi * ((x[0] - a[i])**2 +
(x[1] - b[i])**2)))
#end
g = [0.0] * 1
g[0] = 20.04895 - (x[0] + 2.0)**2 - (x[1] + 1.0)**2
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('Langermann Function 11', objfunc)
opt_prob.addVar('x1', 'c', lower=-2.0, upper=10.0, value=8.0)
opt_prob.addVar('x2', 'c', lower=-2.0, upper=10.0, value=8.0)
opt_prob.addObj('f')
opt_prob.addCon('g', 'i')
print(opt_prob)
# Global Optimization
nsga2 = NSGA2()
nsga2(opt_prob)
print(opt_prob.solution(0))
# Local Optimization Refinement
slsqp = SLSQP()
slsqp(opt_prob.solution(0))
print(opt_prob.solution(0).solution(0))
f = x0**2 + x1**2
g = [0.0] * 2
g[0] = 3 - x0
g[1] = 2 - x1
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
# Instantiate Optimization Problem
opt_prob = Optimization('TOY Constraint Problem', objfunc, use_groups=True)
opt_prob.addVarGroup('a', 2, 'c', value=1.0, lower=0.0, upper=10)
opt_prob.delVarGroup('a')
opt_prob.addVar('x', 'c', value=1.0, lower=0.0, upper=10)
opt_prob.addVarGroup('y', 2, 'c', value=1.0, lower=0.0, upper=10)
opt_prob.delVarGroup('y')
opt_prob.addVarGroup('z', 1, 'c', value=1.0, lower=0.0, upper=10)
opt_prob.addVarGroup('b', 5, 'c', value=3.0, lower=0.0, upper=10)
opt_prob.delVarGroup('b')
opt_prob.addObj('f')
opt_prob.addCon('g1', 'i')
opt_prob.addCon('g2', 'i')
print opt_prob
# Instantiate Optimizer (PSQP) & Solve Problem
slsqp = SLSQP()
y2 = coordinates[:][1]
f = 0
for i in range(N-1):
f += abs(y1[i]*100 - y2[i]*100)**2
g = []
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('CST Parameterization', objfunc)
opt_prob.addVar('x1','c', lower=-2.0,upper=2.0, value=-1.0)
opt_prob.addVar('x2','c', lower=-2.0,upper=2.0, value=-1.0)
opt_prob.addVar('x3','c', lower=-2.0,upper=2.0, value=-1.0)
opt_prob.addVar('x4','c', lower=-2.0,upper=2.0, value=-1.0)
opt_prob.addVar('x5','c', lower=-2.0, upper=2.0, value=1.0)
opt_prob.addVar('x6','c', lower=-2.0, upper=2.0, value=1.0)
opt_prob.addVar('x7','c', lower=-2.0, upper=2.0, value=1.0)
opt_prob.addVar('x8','c', lower=-2.0, upper=2.0, value=1.0)
opt_prob.addObj('f')
print opt_prob
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
slsqp.setOption('IPRINT',-1)
slsqp(opt_prob, sens_type='FD')
# =============================================================================
#
# =============================================================================
def objfunc(x):
f = 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2
g = []
fail = 0
return f, g, fail
# =============================================================================
#
# =============================================================================
opt_prob = Optimization('Rosenbrock Unconstraint Problem', objfunc)
#List of references in the queue with their priority order
opt_prob.addVar('x1', 'c', lower=-10.0, upper=10.0, value=-3.0)
#The Processing capacity
opt_prob.addVar('x2', 'c', lower=-10.0, upper=10.0, value=-4.0)
opt_prob.addObj('f')
#Constraints
#Each queue has a given processing capacity expressed in number of workers
#Each worker can process 35 number of references per week
#Notice period to change the number of workers for one step is 8 weeks
#Max total capacity increase is 20% of nominal capacity
#Fast-lange capacity is fixed equal to 10% planned capacity
#Cost to be added in objective function for revising one date
# is equal to 1000 * number of overdue days
print opt_prob
def objfunc_3(x):
f1 = x[0] - x[1]
f2 = x[0] + x[1]
f = (f1, f2)
fail = 0
g = []
return f,(g, g), (fail, fail)
# =============================================================================
#
# =============================================================================
chord = 1.
x_hinge = 0.75
safety = 0.005*chord
opt_prob = Optimization('main', (objfunc_1, objfunc_2))
opt_prob.addObj("f1")
opt_prob.addObj("f2")
#xs_n
opt_prob.addVar('x1', 'c', lower = -1 , upper = 1, value = 6.817445e-001)
#ys_n
opt_prob.addVar('x2', 'c', lower = -1, upper = 1, value = -5.216475e-001)
#opt_prob.addObj('2', objfunc_2)
print opt_prob
# Global Optimization
nsga2 = NSGA2()
nsga2.setOption('PopSize', 10)
nsga2.setOption('maxGen', 10)
nsga2(opt_prob)
def execute(self):
"""pyOpt execution. Note that pyOpt controls the execution, and the
individual optimizers control the iteration."""
self.pyOpt_solution = None
opt_prob = Optimization(self.title, self.objfunc, var_set={},
obj_set={}, con_set={})
# Add all parameters
for name, param in self.get_parameters().iteritems():
val = param.evaluate()
# We need to identify Enums, Lists, Dicts
metadata = param.get_metadata()[0][1]
# enumerated, discrete or continuous
choices = []
if ('values' in metadata and \
isinstance(metadata['values'],(list, tuple, array, set))):
vartype = 'd'
choices = metadata['values']
elif isinstance(val, bool):
vartype = 'd'
choices = [True, False]
elif isinstance(val, (int, int32, int64)):
vartype = 'i'
elif isinstance(val, (float, float32, float64)):
vartype = 'c'
else:
msg = 'Only continuous, descrete, or enumerated variables ' + \
'are supported. %s is %s.' % (name, type(val))
self.raise_exception(msg, ValueError)
opt_prob.addVar(name, vartype, lower=param.low, upper=param.high,
value=val, choices=choices)
# Add all objectives
for name in self.get_objectives().keys():
opt_prob.addObj(name)
# Add all equality constraints
for name in self.get_eq_constraints().keys():
opt_prob.addCon(name, type='e')
# Add all inequality constraints
for name in self.get_ineq_constraints().keys():
opt_prob.addCon(name, type='i')
# Instantiate the requested optimizer
optimizer = self.optimizer
try:
exec('from pyOpt import %s' % optimizer)
except ImportError:
msg = "Optimizer %s is not avialable in this installation." % \
optimizer
self.raise_exception(msg, ImportError)
optname = vars()[optimizer]
opt = optname()
# Set optimization options
for option, value in self.options.iteritems():
opt.setOption(option, value)
# Execute the optimization problem
if self.differentiator:
# Use OpenMDAO's differentiator for the gradient
opt(opt_prob, sens_type=self.gradfunc)
else:
# Use pyOpt's internal finite difference
opt(opt_prob, sens_type='FD')
# Print results
if self.print_results:
print opt_prob.solution(0)
# Pull optimal parameters back into framework and re-run, so that
# framework is left in the right final state
dvals = []
for i in range(0, len(opt_prob.solution(0)._variables)):
dvals.append(opt_prob.solution(0)._variables[i].value)
self.set_parameters(dvals)
self.run_iteration()
# Save the most recent solution.
self.pyOpt_solution = opt_prob.solution(0)
if __name__ == "__main__":
print "running deterministic optimization "
# Physical problem
rho = 0.2836 # lb/in^3
L = 5.0 # in
P = 25000.0 # lb
E = 30.0e6 # psi
ys = 36260.0 # psi
fs = 1.5
dtruss = TwoBarTruss(rho, L, P, E, ys, fs)
# Optimization Problem
optproblem = TwoBarTrussOpt(MPI.COMM_WORLD, dtruss)
opt_prob = Optimization(args.logfile, optproblem.evalObjCon)
# Add functions
opt_prob.addObj('weight')
opt_prob.addCon('buckling-bar1', type='i')
opt_prob.addCon('failure-bar1', type='i')
opt_prob.addCon('failure-bar2', type='i')
# Add variables
opt_prob.addVar('area-1', type='c', value=1.0, lower=1.0e-3, upper=2.0)
opt_prob.addVar('area-2', type='c', value=1.0, lower=1.0e-3, upper=2.0)
opt_prob.addVar('height', type='c', value=4.0, lower=4.0, upper=10.0)
# Optimization algorithm
if args.algorithm == 'ALGENCAN':
opt = ALGENCAN()
def optimizeTrajectory(self, plot_func=None):
# use non-linear optimization to find parameters for minimal
# condition number trajectory
self.plot_func = plot_func
if self.config['showOptimizationGraph']:
self.initGraph()
## describe optimization problem with pyOpt classes
from pyOpt import Optimization
from pyOpt import ALPSO, SLSQP
# Instanciate Optimization Problem
opt_prob = Optimization('Trajectory optimization', self.objective_func)
opt_prob.addObj('f')
# add variables, define bounds
# w_f - pulsation
opt_prob.addVar('wf', 'c', value=self.wf_init, lower=self.wf_min, upper=self.wf_max)
# q - offsets
for i in range(self.dofs):
opt_prob.addVar('q_%d'%i,'c', value=self.qinit[i], lower=self.qmin[i], upper=self.qmax[i])
# a, b - sin/cos params
for i in range(self.dofs):
for j in range(self.nf[0]):
opt_prob.addVar('a{}_{}'.format(i,j), 'c', value=self.ainit[i][j], lower=self.amin, upper=self.amax)
for i in range(self.dofs):
for j in range(self.nf[0]):
opt_prob.addVar('b{}_{}'.format(i,j), 'c', value=self.binit[i][j], lower=self.bmin, upper=self.bmax)
# add constraint vars (constraint functions are in obfunc)
if self.config['minVelocityConstraint']:
opt_prob.addConGroup('g', self.dofs*5, 'i')
else:
opt_prob.addConGroup('g', self.dofs*4, 'i')
#print opt_prob
initial = [v.value for v in list(opt_prob._variables.values())]
if self.config['useGlobalOptimization']:
### optimize using pyOpt (global)
opt = ALPSO() #augmented lagrange particle swarm optimization
opt.setOption('stopCriteria', 0)
opt.setOption('maxInnerIter', 3)
opt.setOption('maxOuterIter', self.config['globalOptIterations'])
opt.setOption('printInnerIters', 1)
opt.setOption('printOuterIters', 1)
opt.setOption('SwarmSize', 30)
opt.setOption('xinit', 1)
#TODO: how to properly limit max number of function calls?
# no. func calls = (SwarmSize * inner) * outer + SwarmSize
self.iter_max = opt.getOption('SwarmSize') * opt.getOption('maxInnerIter') * opt.getOption('maxOuterIter') + opt.getOption('SwarmSize')
# run fist (global) optimization
try:
#reuse history
opt(opt_prob, store_hst=False, hot_start=True, xstart=initial)
except NameError:
opt(opt_prob, store_hst=False, xstart=initial)
print(opt_prob.solution(0))
### pyOpt local
# after using global optimization, get more exact solution with
# gradient based method init optimizer (only local)
opt2 = SLSQP() #sequential least squares
opt2.setOption('MAXIT', self.config['localOptIterations'])
if self.config['verbose']:
opt2.setOption('IPRINT', 0)
# TODO: amount of function calls depends on amount of variables and iterations to approximate gradient
# iterations are probably steps along the gradient. How to get proper no. of func calls?
self.iter_max = "(unknown)"
if self.config['useGlobalOptimization']:
if self.last_best_sol is not None:
#use best constrained solution
for i in range(len(opt_prob._variables)):
opt_prob._variables[i].value = self.last_best_sol[i]
else:
#reuse previous solution
for i in range(len(opt_prob._variables)):
opt_prob._variables[i].value = opt_prob.solution(0).getVar(i).value
opt2(opt_prob, store_hst=False, sens_step=0.1)
else:
try:
#reuse history
opt2(opt_prob, store_hst=True, hot_start=True, sens_step=0.1)
except NameError:
opt2(opt_prob, store_hst=True, sens_step=0.1)
local_sol = opt_prob.solution(0)
if not self.config['useGlobalOptimization']:
print(local_sol)
local_sol_vec = np.array([local_sol.getVar(x).value for x in range(0,len(local_sol._variables))])
if self.last_best_sol is not None:
local_sol_vec = self.last_best_sol
print("using last best constrained solution instead of given solver solution.")
sol_wf, sol_q, sol_a, sol_b = self.vecToParams(local_sol_vec)
print("testing final solution")
self.iter_cnt = 0
self.objective_func(local_sol_vec)
print("\n")
self.trajectory.initWithParams(sol_a, sol_b, sol_q, self.nf, sol_wf)
if self.config['showOptimizationGraph']:
plt.ioff()
return self.trajectory
