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

Abstract

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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

How to cite

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El 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 -

References

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  9. PJM. Van Laarhoven, EHL. Aarts. Simulated annealing: theory and applications. Dordrecht: D. Reidel Publishing Company, Kluwer, 1987.  
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