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Hybrid Metaheuristics for the Graph Partitioning Problem

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Hybrid Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 434))

Abstract

The Graph Partitioning Problem (GPP) is one of the most studied NP-complete problems notable for its broad spectrum of applicability such as in VLSI design, data mining, image segmentation, etc. Due to its high computational complexity, a large number of approximate approaches have been reported in the literature. Hybrid algorithms that are based on adaptations of popular metaheuristic techniques have shown to provide outstanding performance in terms of partition quality. In particular, it is the hybrids between well-known metaheuristics and multilevel strategies that report partitions of the minimal cut-size value. However, metaheuristic hybrids generally require more computing time than those based on greedy heuristics which can generate partitions of acceptable quality in a matter of seconds even for very large graphs. This chapter is dedicated to a review on some representative hybrid metaheuristic approaches including genetic local search, basic multilevel search and recent development on hybrid multilevel search.

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Authors and Affiliations

  1. LERIA, University of Angers, 2 Bd Lavoisier, 49045, Angers Cedex 01, France

    Una Benlic & Jin-Kao Hao

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  1. Una Benlic
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  2. Jin-Kao Hao
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Correspondence to Una Benlic .

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  1. , Cite Scientifique, University of Lille 1, Bat.M3, Villeneuve d'Ascq, 59655, France

    El-Ghazali Talbi

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Benlic, U., Hao, JK. (2013). Hybrid Metaheuristics for the Graph Partitioning Problem. In: Talbi, EG. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30671-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-30671-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30670-9

  • Online ISBN: 978-3-642-30671-6

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