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Analysis and parameter selection for an adaptive random search algorithm

✍ Scribed by Rajeeva Kumar; Pierre T. Kabamba; David C. Hyland

Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
160 KB
Volume
68
Category
Article
ISSN
0378-4754

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✦ Synopsis

This paper presents an analysis of an adaptive random search (ARS) algorithm, a global minimization method. A probability model is introduced to characterize the statistical properties of the number of iterations required to find an acceptable solution. Moreover, based on this probability model, a new stopping criterion is introduced to predict the maximum number of iterations required to find an acceptable solution with a pre-specified level of confidence. Finally, this paper presents a systematic procedure for choosing the user-specified parameters in the ARS algorithm for fastest convergence. The results, which are valid for search spaces of arbitrary dimensions, are illustrated on a simple three-dimensional example.

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