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

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  • Georgieva, A.
  • Jordanov, I.

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.

Suggested Citation

  • Georgieva, A. & Jordanov, I., 2009. "Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms," European Journal of Operational Research, Elsevier, vol. 196(2), pages 413-422, July.
    • Handle: RePEc:eee:ejores:v:196:y:2009:i:2:p:413-422
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    References listed on IDEAS

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    1. Chelouah, Rachid & Siarry, Patrick, 2003. "Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions," European Journal of Operational Research, Elsevier, vol. 148(2), pages 335-348, July.
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    Cited by:

    1. Hao Zhang & Yan Cui & Hepu Deng & Shuxian Cui & Huijia Mu, 2021. "An Improved Genetic Algorithm for the Optimal Distribution of Fresh Products under Uncertain Demand," Mathematics, MDPI, vol. 9(18), pages 1-18, September.
    2. Gomes, J.H.F. & Paiva, A.P. & Costa, S.C. & Balestrassi, P.P. & Paiva, E.J., 2013. "Weighted Multivariate Mean Square Error for processes optimization: A case study on flux-cored arc welding for stainless steel claddings," European Journal of Operational Research, Elsevier, vol. 226(3), pages 522-535.
    3. Marco Locatelli & Fabio Schoen, 2016. "Global optimization based on local searches," Annals of Operations Research, Springer, vol. 240(1), pages 251-270, May.

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