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Graph Partitioning via Recurrent Multivalued Neural Networks

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Book cover Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

In this work, the well-known Graph Partitioning (GP) problem for undirected weighted graphs has been studied from two points of view: maximizing (MaxCut) or minimizing (MinCut) the cost of the cut induced in the graph by the partition. An unified model, based on a neural technique for optimization problems, has been applied to these two concrete problems. A detailed description of the model is presented, and the technique to minimize an energy function, that measures the goodness of solutions, is fully described. Some techniques to escape from local optima are presented as well. It has proved to be a very competitive and efficient algorithm, in terms of quality of solutions and computational time, when compared to the state-of-the-art methods. Some simulation results are presented in this paper, to show the comparative efficiency of the methods.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, University of Málaga, Málaga, Spain

    Enrique Mérida-Casermeiro & Domingo López-Rodríguez

Authors
  1. Enrique Mérida-Casermeiro
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  2. Domingo López-Rodríguez
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Mérida-Casermeiro, E., López-Rodríguez, D. (2005). Graph Partitioning via Recurrent Multivalued Neural Networks. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_141

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  • DOI: https://doi.org/10.1007/11494669_141

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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