NUMERICAL STUDIES OF SOME GENERALIZED CONTROLLED RANDOM SEARCH ALGORITHMS

P. Kaelo and M. M. Ali ()
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P. Kaelo: Department of Mathematics, University of Botswana, Private Bag UB00704, Gaborone, Botswana
M. M. Ali: School of Computational and Applied Mathematics, Witwatersrand University, Wits 2050, Johannesburg, South Africa

Asia-Pacific Journal of Operational Research (APJOR), 2012, vol. 29, issue 02, 1-24

Abstract: This paper presents motivations and algorithmic details of some generalized controlled random search (CRS) algorithms for global optimization. It also carries out an extensive numerical study of the generalized CRS algorithms to demonstrate their superiorities over their original counterparts. The numerical study is carried out using a set of 50 test problems many of which are inspired by practical applications. Numerical experiments indicate that the generalized algorithms are considerably better than the previous versions. The algorithms are also compared with the DIRECT algorithm (Jones et al., 1993). The comparison shows that the generalized CRS algorithms are better than the DIRECT algorithm in high dimensional problems. Thus, they offer a reasonable alternative to many currently available stochastic algorithms, especially for problems requiring "direct search type" methods.

Keywords: Global optimization; DIRECT algorithm; controlled random search; linear interpolation; probabilistic adaptation (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1142/S0217595912500169

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