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The Max-Min ANT System and Local Search for Combinatorial Optimization Problems

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Meta-Heuristics

Abstract

In this paper we present an extension of the \(\mathcal{M}\mathcal{A}\mathcal{X} - \mathcal{M}\mathcal{I}\mathcal{N}\) Ant System and apply it to Traveling Salesman Problems and Quadratic Assignment Problems. The extension involves the use of a modified choice rule and a hybrid scheme allowing ants to improve their solution by local search. The computational results show that this algorithm can be used to efficiently find near-optimal solutions to hard combinatorial optimization problems and that it is one of the best methods for the solution of structured quadratic assignment problems.

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Author information

Authors and Affiliations

  1. TU Darmstadt, FB Informatik, FG Intellektik, Alexanderstr. 10, 64283, Darmstadt, Germany

    Thomas Stützle & Holger Hoos

Authors
  1. Thomas Stützle
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  2. Holger Hoos
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Editor information

Editors and Affiliations

  1. Technical University of Braunschweig, Germany

    Stefan Voß

  2. University of Bologna, Italy

    Silvano Martello

  3. University of Kent, UK

    Ibrahim H. Osman

  4. University of Versailles, France

    Catherine Roucairol

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© 1999 Springer Science+Business Media New York

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Stützle, T., Hoos, H. (1999). The Max-Min ANT System and Local Search for Combinatorial Optimization Problems. In: Voß, S., Martello, S., Osman, I.H., Roucairol, C. (eds) Meta-Heuristics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5775-3_22

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  • DOI: https://doi.org/10.1007/978-1-4615-5775-3_22

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7646-0

  • Online ISBN: 978-1-4615-5775-3

  • eBook Packages: Springer Book Archive

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