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Analysis and parameter selection for an Adaptive Random Search algorithm

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3 Author(s)
Kumar, R. ; Dept. of Aerosp. Eng., Michigan Univ., Ann Arbor, MI, USA ; Kabamba, P.T. ; Hyland, D.C.

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. This leads to the Monte Carlo version of the algorithm. 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 3-dimensional example.

Published in:

Decision and Control, 2004. CDC. 43rd IEEE Conference on  (Volume:5 )

Date of Conference:

14-17 Dec. 2004