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A Global Stochastic Optimization Method for Large ScaleProblems

Published online by Cambridge University Press:  26 August 2010

A. Taik ,
W. El Alem ,
A. El Hami  and
R. Ellaia
W. El Alem*
Affiliation:
Laboratory of study and research in applied mathematics, Mohammed V University EMI, BP. 765, Ibn Sina avenue, Agdal Rabat, Morocco Laboratory of mechanics of Rouen, national institute for applied sciences, Rouen BP 08, university avenue 76801 St Etienne du Rouvray Cedex, France
A. El Hami
Affiliation:
Laboratory of mechanics of Rouen, national institute for applied sciences, Rouen BP 08, university avenue 76801 St Etienne du Rouvray Cedex, France
R. Ellaia
Affiliation:
Laboratory of study and research in applied mathematics, Mohammed V University EMI, BP. 765, Ibn Sina avenue, Agdal Rabat, Morocco
*
* Corresponding author: E-mail:welalem@yahoo.fr
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Abstract

In this paper, a new hybrid simulated annealing algorithm for constrained globaloptimization is proposed. We have developed a stochastic algorithm called ASAPSPSA thatuses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to thebasic simulated annealing algorithm (SA) that gives the region containing the globalsolution of an objective function. In addition, Simultaneous Perturbation StochasticApproximation (SPSA) method, for solving unconstrained optimization problems, is used torefine the solution. We also propose Penalty SPSA (PSPSA) for solving constrainedoptimization problems. The constraints are handled using exterior point penalty functions.The combination of both techniques ASA and PSPSA provides a powerful hybrid optimizationmethod. The proposed method has a good balance between exploration and exploitation withvery fast computation speed, its performance as a viable large scale optimization methodis 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 successfulresults were obtained

Keywords

global optimizationstructural optimizationsimultaneous perturbation stochastic approximation

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