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Revisiting the Self-adaptive Large Neighborhood Search

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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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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Authors and Affiliations

  1. ICTEAM institute, Universite catholique de Louvain, Louvain-la-Neuve, Belgium

    Charles Thomas & Pierre Schaus

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  1. Charles Thomas
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  2. Pierre Schaus
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Correspondence to Charles Thomas .

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  1. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Willem-Jan van Hoeve

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Thomas, C., Schaus, P. (2018). Revisiting the Self-adaptive Large Neighborhood Search. In: van Hoeve, WJ. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2018. Lecture Notes in Computer Science(), vol 10848. Springer, Cham. https://doi.org/10.1007/978-3-319-93031-2_40

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  • DOI: https://doi.org/10.1007/978-3-319-93031-2_40

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  • Online ISBN: 978-3-319-93031-2

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