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A hybrid meta-heuristic for global optimisation using low-discrepancy sequences of points

Authors
Journal
Computers & Operations Research
0305-0548
Publisher
Elsevier
Publication Date
Volume
37
Issue
3
Identifiers
DOI: 10.1016/j.cor.2008.07.004
Keywords
  • Global Optimisation
  • Meta-Heuristics
  • Hybrid Methods
  • Local Minima Problem
  • Low-Discrepancy Sequences
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract A hybrid novel meta-heuristic technique for bound-constrained global optimisation (GO) is proposed in this paper. We have developed an iterative algorithm called LP τ Optimisation ( LP τ O ) that uses low-discrepancy sequences of points and meta-heuristic knowledge to find regions of attraction when searching for a global minimum of an objective function. Subsequently, the well-known Nelder–Mead ( NM ) simplex local search is used to refine the solution found by the LP τ O method. The combination of the two techniques ( LP τ O and NM ) provides a powerful hybrid optimisation technique, which we call LP τ NM . Its properties—applicability, convergence, consistency and stability are discussed here in detail. The LP τ NM is tested on a number of benchmark multimodal mathematical functions from 2 to 20 dimensions and compared with results from other stochastic heuristic methods.

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