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Combining Genetic Algorithm with Variable Neighborhood Search for MAX-SAT

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Innovative Computing, Optimization and Its Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 741))

Abstract

Variable Neighborhood Search (VNS) is a simple meta-heuristic that systematically changes the size and type of neighborhood during the search process in order to escape from local optima. In this paper, a variable-neighborhood-genetic-based-algorithm is proposed for the maximum satisfiability problem (MAX-SAT). Most of the work published earlier on VNS starts from the first neighborhood and moves on to higher neighborhoods without controlling and adapting the ordering of neighborhood structures. The order in which the neighborhood structures have been proposed in this work enables the genetic algorithm with a better mechanism for performing diversification and intensification. A set of benchmark problem instances is used to compare the effectiveness of the proposed algorithm against the standard genetic algorithm. This paper reports promising results when the proposed hybrid algorithm is compared with state-of-the art solvers.

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Acknowledgements

We would like to address a particular warm thank to the members of the organizing committee and scientific committee for making the First EAI International Conference on Computer Science and Engineering, NOVEMBER 11–12, 2016, PENANG, MALAYSIA a great success.

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Authors and Affiliations

  1. Department of Maritime Technology and Innovation, University College Southeast, Notodden, Norway

    Noureddine Bouhmala & Kjell Ivar Øvergård

Authors
  1. Noureddine Bouhmala
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  2. Kjell Ivar Øvergård
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Corresponding author

Correspondence to Noureddine Bouhmala .

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Editors and Affiliations

  1. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB-TU Ostrava, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

  2. Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Teronoh, Perak, Malaysia

    Pandian Vasant

  3. Ton Duc Thang University, Ho Chi Minh, Vietnam

    Vo Hoang Duy

  4. Ton Duc Thang University, Ho Chi Minh, Vietnam

    Tran Trong Dao

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Bouhmala, N., Øvergård, K.I. (2018). Combining Genetic Algorithm with Variable Neighborhood Search for MAX-SAT. In: Zelinka, I., Vasant, P., Duy, V., Dao, T. (eds) Innovative Computing, Optimization and Its Applications. Studies in Computational Intelligence, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-319-66984-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-66984-7_5

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  • Online ISBN: 978-3-319-66984-7

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