Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms

A. Georgieva and I. Jordanov

European Journal of Operational Research, 2009, vol. 196, issue 2, 413-422

Abstract: In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLP[tau]S that uses genetic algorithms, LP[tau] low-discrepancy sequences of points and heuristic rules to find regions of attraction when searching a global minimum of an objective function. Subsequently Nelder-Mead Simplex local search technique is used to refine the solution. The combination of the three techniques (Genetic algorithms, LP[tau]O Low-discrepancy search and Simplex search) provides a powerful hybrid heuristic optimization method which is tested on a number of benchmark multimodal functions with 10-150 dimensions, and the method properties - applicability, convergence, consistency and stability are discussed in detail.

Keywords: Global; optimization; Genetic; algorithms; Heuristics; Low-discrepancy; sequences; Hybrid; methods (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:196:y:2009:i:2:p:413-422

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