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Solving the clique partitioning problem as a maximally diverse grouping problem

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Abstract

In this paper we show that the clique partitioning problem can be reformulated in an equivalent form as the maximally diverse grouping problem (MDGP). We then modify a skewed general variable neighborhood search (SGVNS) heuristic that was first developed to solve the MDGP. Similarly as with the MDGP, significant improvements over the state of the art are obtained when SGVNS is tested on large scale instances. This further confirms the usefulness of a combined approach of diversification afforded with skewed VNS and intensification afforded with the local search in general VNS.

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Acknowledgments

This research has been supported in part by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (NSERC #205041-2008) and by Serbian Ministry of Sciences, Project #174010. This work is also partly supported by RSF (Russian Federation) grant 14-41-00039.

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Authors and Affiliations

  1. Royal Military College of Canada, Kingston, Canada

    Jack Brimberg

  2. Mathematical Institute SANU, Belgrade, Serbia

    Stefana Janićijević & Dragan Urošević

  3. LAMIH, Université de Valenciennes, Valenciennes, France

    Nenad Mladenović

Authors
  1. Jack Brimberg
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  2. Stefana Janićijević
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  3. Nenad Mladenović
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  4. Dragan Urošević
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Correspondence to Dragan Urošević.

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Brimberg, J., Janićijević, S., Mladenović, N. et al. Solving the clique partitioning problem as a maximally diverse grouping problem. Optim Lett 11, 1123–1135 (2017). https://doi.org/10.1007/s11590-015-0869-4

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  • DOI: https://doi.org/10.1007/s11590-015-0869-4

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