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A Post-optimization Method to Improve the Ant Colony System Algorithm

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Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living (IWANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5518))

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Abstract

Ant Colony Optimization is a metaheuristic which has been successfully applied to solve several NP-hard problems. It includes several algorithms which imitate the behavior of natural ants. The algorithm called Ant Colony System is one of the best-performing ant-based algorithms. In this paper we present an enhanced algorithm, which applies dynamic programming to improve the solution generated by the ants. The method is applied to the well-known Traveling Salesman Problem. We present computational results that show the improvement obtained with the modified algorithm.

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References

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Author information

Authors and Affiliations

  1. Universidad de Salamanca. Escuela Politécnica Superior de Zamora, Av. Requejo, 33, C.P. 49022, Zamora, Spain

    M. L. Pérez-Delgado & J. Escuadra Burrieza

Authors
  1. M. L. Pérez-Delgado
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  2. J. Escuadra Burrieza
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Editor information

Editors and Affiliations

  1. Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

    Sigeru Omatu

  2. Department of Informatics / CCTC, University of Minho, Braga, Portugal

    Miguel P. Rocha

  3. MAmI Research Lab, University of Castilla-La Mancha,, Ciudad Real, Spain

    José Bravo

  4. Department of Informatics, University of Vigo, Ourense, Spain

    Florentino Fernández

  5. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  6. Higher Polytechnic School, University of Burgos, Burgos, Spain

    Andrés Bustillo

  7. Department of Informatics, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

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© 2009 Springer-Verlag Berlin Heidelberg

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Pérez-Delgado, M.L., Burrieza, J.E. (2009). A Post-optimization Method to Improve the Ant Colony System Algorithm. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_60

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  • DOI: https://doi.org/10.1007/978-3-642-02481-8_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02480-1

  • Online ISBN: 978-3-642-02481-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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