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Solver-Independent Large Neighbourhood Search

  • Jip J. Dekker
  • Maria Garcia de la Banda
  • Andreas Schutt
  • Peter J. Stuckey
  • Guido Tack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11008)

Abstract

The combination of large neighbourhood search (LNS) methods with complete search methods has proved to be very effective. By restricting the search to (small) areas around an existing solution, the complete method is often able to quickly improve its solutions. However, developing such a combined method can be time-consuming: While the model of a problem can be expressed in a high-level solver-independent language, the LNS search strategies typically need to be implemented in the search language of the target constraint solvers. In this paper we show how we can simplify this process by (a) extending constraint modelling languages to support solver-independent LNS search definitions, and (b) defining small solver extensions that allow solvers to implement these solver-independent LNS searches. Modellers can then implement an LNS search to be executed in any extended solver, by simply using the modelling language constructs. Experiments show that the resulting LNS searches only introduce a small overhead compared to direct implementations in the search language of the underlying solvers.

Notes

Acknowledgements

This research was partly sponsored by the Australian Research Council grant DP180100151.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Monash UniversityMelbourneAustralia
  2. 2.Data61, CSIROMelbourneAustralia

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