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Analysis and parameter selection for an adaptive random search algorithm

Author

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

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.

Suggested Citation

  • Kumar, Rajeeva & Kabamba, Pierre T. & Hyland, David C., 2005. "Analysis and parameter selection for an adaptive random search algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 95-103.
    • Handle: RePEc:eee:matcom:v:68:y:2005:i:2:p:95-103
    DOI: 10.1016/j.matcom.2004.10.002
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    References listed on IDEAS

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    1. Pronzato, Luc & Walter, Eric & Venot, Alain & Lebruchec, Jean-Francois, 1984. "A general-purpose global optimizer: Implimentation and applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 26(5), pages 412-422.
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