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Cutting Graphs Using Competing Ant Colonies and an Edge Clustering Heuristic

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6622))

Abstract

We investigate the usage of Ant Colony Optimization to detect balanced graph cuts. In order to do so we develop an algorithm based on competing ant colonies. We use a heuristic from social network analysis called the edge clustering coefficient, which greatly helps our colonies in local search. The algorithm is able to detect cuts that correspond very well to known cuts on small real-world networks. Also, with the correct parameter balance, our algorithm often outperforms the traditional Kernighan-Lin algorithm for graph partitioning with equal running time complexity. On larger networks, our algorithm is able to obtain low cut sizes, but at the cost of a balanced partition.

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Author information

Authors and Affiliations

  1. Radboud University, Nijmegen, Netherlands

    Max Hinne & Elena Marchiori

Authors
  1. Max Hinne
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  2. Elena Marchiori
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Editor information

Editors and Affiliations

  1. Department of Business Administration and Computer Science, University of Applied Sciences and Arts, Ricklinger Stadtweg 120, 30459, Hannover, Germany

    Peter Merz

  2. Faculty of Sciences, University of Angers, 2, Boulevard Lavoisier, Cedex 01, 49045, Angers, France

    Jin-Kao Hao

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© 2011 Springer-Verlag Berlin Heidelberg

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Hinne, M., Marchiori, E. (2011). Cutting Graphs Using Competing Ant Colonies and an Edge Clustering Heuristic. In: Merz, P., Hao, JK. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2011. Lecture Notes in Computer Science, vol 6622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20364-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-20364-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20363-3

  • Online ISBN: 978-3-642-20364-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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