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Empirical studies of heuristic local search for constraint solving

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Principles and Practice of Constraint Programming — CP96 (CP 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1118))

Abstract

The goal of this paper is twofold. First, we introduce a class of local search procedures for solving optimization and constraint problems. These procedures are based on various heuristics for choosing variables and values in order to examine a general neighborhood. Second, four combinations of heuristics are empirically evaluated by using the graph-coloring problem and a real world application — the frequency assignment problem. The results are also compared with those obtained with other approaches including simulated annealing, Tabu search, constraint programming and heuristic graph coloring algorithms. Empirical evidence shows the benefits of this class of local search procedures for solving large and hard instances.

Work partially supported by the CNET (French National Research Center for Telecommunications) under the grant No.940B006-01.

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Author information

Authors and Affiliations

  1. Parc Scientifique Georges Besse, LGI2P EMA-EERIE, F-30000, Nîmes, France

    Jin-Kao Hao & Raphaël Dorne

Authors
  1. Jin-Kao Hao
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  2. Raphaël Dorne
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Eugene C. Freuder

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© 1996 Springer-Verlag Berlin Heidelberg

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Hao, JK., Dorne, R. (1996). Empirical studies of heuristic local search for constraint solving. In: Freuder, E.C. (eds) Principles and Practice of Constraint Programming — CP96. CP 1996. Lecture Notes in Computer Science, vol 1118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61551-2_75

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  • DOI: https://doi.org/10.1007/3-540-61551-2_75

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61551-4

  • Online ISBN: 978-3-540-70620-5

  • eBook Packages: Springer Book Archive

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