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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
#
# Adapted from parallel_desolve.py
__doc__ = """
# Tests MP version of Storn and Price's Polynomial 'Fitting' Problem.
#
# Exact answer: [1,1,1]
# Reference:
#
# [1] Storn, R. and Price, K. Differential Evolution - A Simple and Efficient
# Heuristic for Global Optimization over Continuous Spaces. Journal of Global
# Optimization 11: 341-359, 1997.
# To run in parallel: (must install 'pathos')
python mpmap_desolve_rosen.py
"""
try:
from pathos.pools import ProcessPool as Pool
except:
print(__doc__)
from mystic.solvers import DifferentialEvolutionSolver2
from mystic.termination import ChangeOverGeneration, VTR
from mystic.strategy import Best1Exp
from mystic.monitors import VerboseMonitor
from mystic.tools import random_seed
#from raw_rosen import rosen as myCost # with a helper function
from mystic.models import rosen as myCost # without a helper function
ND = 3
NP = 20
MAX_GENERATIONS = NP*NP
NNODES = 20
TOL = 0.01
CROSS = 0.9
SCALE = 0.8
seed = 100
if __name__=='__main__':
from pathos.helpers import freeze_support, shutdown
freeze_support() # help Windows use multiprocessing
def print_solution(func):
print(func)
return
psow = VerboseMonitor(10)
ssow = VerboseMonitor(10)
random_seed(seed)
print("first sequential...")
solver = DifferentialEvolutionSolver2(ND,NP) #XXX: sequential
solver.SetRandomInitialPoints(min=[-100.0]*ND, max=[100.0]*ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(ssow)
solver.Solve(myCost, VTR(TOL), strategy=Best1Exp, \
CrossProbability=CROSS, ScalingFactor=SCALE, disp=1)
print("")
print_solution( solver.bestSolution )
random_seed(seed)
print("\n and now parallel...")
solver2 = DifferentialEvolutionSolver2(ND,NP) #XXX: parallel
solver2.SetMapper(Pool(NNODES).map)
solver2.SetRandomInitialPoints(min=[-100.0]*ND, max=[100.0]*ND)
solver2.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver2.SetGenerationMonitor(psow)
solver2.Solve(myCost, VTR(TOL), strategy=Best1Exp, \
CrossProbability=CROSS, ScalingFactor=SCALE, disp=1)
print("")
print_solution( solver2.bestSolution )
shutdown() # help multiprocessing shutdown all workers
# end of file
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