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

A. Georgieva, Ivan Jordanov

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Abstract

In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLPτS that uses genetic algorithms, LPτ 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, LPτO 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 to 150 dimensions, and the method properties - applicability, convergence, consistency and stability are discussed in detail.
Original languageEnglish
Pages (from-to)413-422
Number of pages10
JournalEuropean Journal of Operational Research
Volume196
Issue number2
DOIs
Publication statusPublished - 2009

Keywords

  • global optimization
  • genetic algorithms
  • heuristics
  • low-discrepancy sequences
  • hybrid methods

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