A Global Stochastic Optimization Method for Large Scale Problems
W. El Alem; A. El Hami; R. Ellaia
Mathematical Modelling of Natural Phenomena (2010)
- Volume: 5, Issue: 7, page 97-102
- ISSN: 0973-5348
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topEl Alem, W., El Hami, A., and Ellaia, R.. Taik, A., ed. "A Global Stochastic Optimization Method for Large Scale Problems." Mathematical Modelling of Natural Phenomena 5.7 (2010): 97-102. <http://eudml.org/doc/197674>.
@article{ElAlem2010,
abstract = {In this paper, a new hybrid simulated annealing algorithm for constrained global
optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that
uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the
basic simulated annealing algorithm (SA) that gives the region containing the global
solution of an objective function. In addition, Simultaneous Perturbation Stochastic
Approximation (SPSA) method, for solving unconstrained optimization problems, is used to
refine the solution. We also propose Penalty SPSA (PSPSA) for solving constrained
optimization problems. The constraints are handled using exterior point penalty functions.
The combination of both techniques ASA and PSPSA provides a powerful hybrid optimization
method. The proposed method has a good balance between exploration and exploitation with
very fast computation speed, its performance as a viable large scale optimization method
is demonstrated by testing it on a number of benchmark functions with 2 - 500 dimensions.
In addition, applicability of the algorithm on structural design was tested and successful
results were obtained},
author = {El Alem, W., El Hami, A., Ellaia, R.},
editor = {Taik, A.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {global optimization; structural optimization; simultaneous perturbation stochastic approximation},
language = {eng},
month = {8},
number = {7},
pages = {97-102},
publisher = {EDP Sciences},
title = {A Global Stochastic Optimization Method for Large Scale Problems},
url = {http://eudml.org/doc/197674},
volume = {5},
year = {2010},
}
TY - JOUR
AU - El Alem, W.
AU - El Hami, A.
AU - Ellaia, R.
AU - Taik, A.
TI - A Global Stochastic Optimization Method for Large Scale Problems
JO - Mathematical Modelling of Natural Phenomena
DA - 2010/8//
PB - EDP Sciences
VL - 5
IS - 7
SP - 97
EP - 102
AB - In this paper, a new hybrid simulated annealing algorithm for constrained global
optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that
uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the
basic simulated annealing algorithm (SA) that gives the region containing the global
solution of an objective function. In addition, Simultaneous Perturbation Stochastic
Approximation (SPSA) method, for solving unconstrained optimization problems, is used to
refine the solution. We also propose Penalty SPSA (PSPSA) for solving constrained
optimization problems. The constraints are handled using exterior point penalty functions.
The combination of both techniques ASA and PSPSA provides a powerful hybrid optimization
method. The proposed method has a good balance between exploration and exploitation with
very fast computation speed, its performance as a viable large scale optimization method
is demonstrated by testing it on a number of benchmark functions with 2 - 500 dimensions.
In addition, applicability of the algorithm on structural design was tested and successful
results were obtained
LA - eng
KW - global optimization; structural optimization; simultaneous perturbation stochastic approximation
UR - http://eudml.org/doc/197674
ER -
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