Source code for pyoptsparse.pyNSGA2.pyNSGA2

pyNSGA2 - A variation of the pyNSGA2 wrapper specificially designed to
work with sparse optimization problems.
# Compiled module
    from . import nsga2  # isort: skip
except ImportError:
    nsga2 = None
# Standard Python modules
import time

# External modules
import numpy as np

# Local modules
from ..pyOpt_error import Error
from ..pyOpt_optimizer import Optimizer

[docs]class NSGA2(Optimizer): """ NSGA2 Optimizer Class - Inherited from Optimizer Abstract Class """ def __init__(self, raiseError=True, options={}): name = "NSGA-II" category = "Global Optimizer" defOpts = self._getDefaultOptions() informs = self._getInforms() super().__init__(name, category, defaultOptions=defOpts, informs=informs, options=options) if nsga2 is None: if raiseError: raise Error("There was an error importing the compiled nsga2 module") @staticmethod def _getInforms(): informs = {} return informs @staticmethod def _getDefaultOptions(): defOpts = { "PopSize": [int, 100], "maxGen": [int, 1000], "pCross_real": [float, 0.6], "pMut_real": [float, 0.2], "eta_c": [float, 10.0], "eta_m": [float, 20.0], "pCross_bin": [float, 0.0], "pMut_bin": [float, 0.0], "PrintOut": [int, 1], "seed": [int, 0], "xinit": [int, 0], } return defOpts
[docs] def __call__(self, optProb, storeHistory=None, hotStart=None, **kwargs): """ This is the main routine used to solve the optimization problem. Parameters ---------- optProb : Optimization or Solution class instance This is the complete description of the optimization problem to be solved by the optimizer storeHistory : str File name of the history file into which the history of this optimization will be stored hotStart : str File name of the history file to "replay" for the optimziation. The optimization problem used to generate the history file specified in 'hotStart' must be **IDENTICAL** to the currently supplied 'optProb'. By identical we mean, **EVERY SINGLE PARAMETER MUST BE IDENTICAL**. As soon as he requested evaluation point from NSGA2 does not match the history, function and gradient evaluations revert back to normal evaluations. Notes ----- The kwargs are there such that the sens= argument can be supplied (but ignored here in nsga2) """ # ====================================================================== # NSGA-II - Objective/Constraint Values Function # ====================================================================== def objconfunc(nreal, nobj, ncon, x, f, g): xx = np.array(x) fobj, fcon, fail = self._masterFunc(xx, ["fobj", "fcon"]) fobj = np.atleast_1d(fobj) f[0:nobj] = fobj g[0:ncon] = -fcon[0:ncon] return f, g self.startTime = time.time() self.callCounter = 0 if len(optProb.constraints) == 0: # If the user *actually* has an unconstrained problem, # slsqp sort of chokes with has to have at # least one constraint. So we will add one # automatically here: self.unconstrained = True optProb.dummyConstraint = False # Save the optimization problem and finalize constraint # Jacobian, in general can only do on root proc self.optProb = optProb self.optProb.finalize() # Set history/hotstart self._setHistory(storeHistory, hotStart) self._setInitialCacheValues() blx, bux, xs = self._assembleContinuousVariables() xs = np.maximum(xs, blx) xs = np.minimum(xs, bux) n = len(xs) ff = self._assembleObjective() oneSided = True # Set the number of nonlinear constraints snopt *thinks* we have: if self.unconstrained: m = 0 else: indices, blc, buc, fact = self.optProb.getOrdering( ["ne", "le", "ni", "li"], oneSided=oneSided, noEquality=True ) m = len(indices) self.optProb.jacIndices = indices self.optProb.fact = fact self.optProb.offset = buc g = nsga2.new_doubleArray(m) len_ff = len(np.atleast_1d(ff)) f = nsga2.new_doubleArray(len_ff) if self.optProb.comm.rank == 0: # Variables Handling n = len(xs) x = nsga2.new_doubleArray(n) xl = nsga2.new_doubleArray(n) xu = nsga2.new_doubleArray(n) for i in range(n): nsga2.doubleArray_setitem(x, i, xs[i]) nsga2.doubleArray_setitem(xl, i, blx[i]) nsga2.doubleArray_setitem(xu, i, bux[i]) # Setup argument list values nfeval = 0 opt = self.getOption if self.getOption("PrintOut") >= 0 and self.getOption("PrintOut") <= 2: printout = self.getOption("PrintOut") else: raise Error("Incorrect option PrintOut") seed = self.getOption("seed") if seed == 0: seed = time.time() # Run NSGA-II nsga2.set_pyfunc(objconfunc) t0 = time.time() # fmt: off nsga2.nsga2(n, m, len_ff, f, x, g, nfeval, xl, xu, opt('PopSize'), opt('maxGen'), opt('pCross_real'), opt('pMut_real'), opt('eta_c'), opt('eta_m'), opt('pCross_bin'), opt('pMut_bin'), printout, seed, opt('xinit')) # fmt: on optTime = time.time() - t0 # Broadcast a -1 to indcate NSGA2 has finished self.optProb.comm.bcast(-1, root=0) # Store Results sol_inform = {} # sol_inform['value'] = inform # sol_inform['text'] = self.informs[inform[0]] xstar = [0.0] * n for i in range(n): xstar[i] = nsga2.doubleArray_getitem(x, i) # Create the optimization solution sol = self._createSolution(optTime, sol_inform, ff, xstar) else: # We are not on the root process so go into waiting loop: self._waitLoop() sol = None # Communication solution and return sol = self._communicateSolution(sol) return sol