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A Study into Ant Colony Optimisation, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3004))

Abstract

We compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, so that it can actually compete with constraint programming. The resampling ratio is used to provide insight into heuristic algorithms performances. Regarding efficiency, we show that if constraint programming is the fastest when instances have a low number of variables, ant colony optimisation becomes faster when increasing the number of variables.

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Author information

Authors and Affiliations

  1. CWI, P.O. Box 94079, NL-1090 GB, Amsterdam, The Netherlands

    Jano I. van Hemert

  2. LIRIS, CNRS FRE 2672, University of Lyon 1, Nautibus, 43 bd du 11 novembre, 69 622, Villeurbanne cedex, France

    Christine Solnon

Authors
  1. Jano I. van Hemert
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  2. Christine Solnon
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Editors and Affiliations

  1. SAP AG, Neurottstr. 16, 69190, Walldorf, Germany

    Jens Gottlieb

  2. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

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© 2004 Springer-Verlag Berlin Heidelberg

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van Hemert, J.I., Solnon, C. (2004). A Study into Ant Colony Optimisation, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems. In: Gottlieb, J., Raidl, G.R. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2004. Lecture Notes in Computer Science, vol 3004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24652-7_12

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  • DOI: https://doi.org/10.1007/978-3-540-24652-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21367-3

  • Online ISBN: 978-3-540-24652-7

  • eBook Packages: Springer Book Archive

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