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#!/usr/bin/env python
#
# Problem definition:
# A-R Hedar and M Fukushima, "Derivative-Free Filter Simulated Annealing
# Method for Constrained Continuous Global Optimization", Journal of
# Global Optimization, 35(4), 521-549 (2006).
#
# Original Matlab code written by A. Hedar (Nov. 23, 2005)
# http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/go.htm
# and ported to Python by Mike McKerns (December 2014)
#
# 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
"a Tension-Compression String"
from spring import objective, bounds, xs, ys
from mystic.constraints import as_constraint
from mystic.penalty import quadratic_inequality
def penalty1(x): # <= 0.0
return 1.0 - (x[1]**3 * x[2])/(71785*x[0]**4)
def penalty2(x): # <= 0.0
return (4*x[1]**2 - x[0]*x[1])/(12566*x[0]**3 * (x[1] - x[0])) + 1./(5108*x[0]**2) - 1.0
def penalty3(x): # <= 0.0
return 1.0 - 140.45*x[0]/(x[2] * x[1]**2)
def penalty4(x): # <= 0.0
return (x[0] + x[1])/1.5 - 1.0
@quadratic_inequality(penalty1, k=1e12)
@quadratic_inequality(penalty2, k=1e12)
@quadratic_inequality(penalty3, k=1e12)
@quadratic_inequality(penalty4, k=1e12)
def penalty(x):
return 0.0
solver = as_constraint(penalty)
if __name__ == '__main__':
from mystic.solvers import diffev2
from mystic.math import almostEqual
result = diffev2(objective, x0=bounds, bounds=bounds, penalty=penalty, npop=40, gtol=500, disp=False, full_output=True)
assert almostEqual(result[0], xs, rel=1e-2)
assert almostEqual(result[1], ys, rel=1e-2)
# EOF
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