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Revisiting the Self-adaptive Large Neighborhood Search

  • Charles Thomas
  • Pierre Schaus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)

Abstract

This paper revisits the Self-Adaptive Large Neighborhood Search introduced by Laborie and Godard. We propose a variation in the weight-update mechanism especially useful when the LNS operators available in the portfolio exhibit unequal running times. We also propose some generic relaxations working for a large family of problems in a black-box fashion. We evaluate our method on various problem types demonstrating that our approach converges faster toward a selection of efficient operators.

Notes

Acknowledgements

We thank the reviewers for their feedback. This work was funded by the Walloon Region (Belgium) as part of the PRESupply project.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ICTEAM instituteUniversite catholique de LouvainLouvain-la-NeuveBelgium

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