Analysis and parameter selection for an adaptive random search algorithm

Rajeeva Kumar, Pierre T. Kabamba and David C. Hyland

Mathematics and Computers in Simulation (MATCOM), 2005, vol. 68, issue 2, 95-103

Abstract: 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.

Keywords: Global optimization; ARS algorithm; Stopping rule (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:68:y:2005:i:2:p:95-103

DOI: 10.1016/j.matcom.2004.10.002

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