Revisiting the self-adaptive large neighborhood search

Thomas, Charles;Schaus, Pierre
(2018) 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research — Location: Delft (26.June.2018)

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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. © Springer International Publishing AG, part of Springer Nature 2018.
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Thomas, C., & Schaus, P. (2018). Revisiting the self-adaptive large neighborhood search. Lecture Notes in Computer Science, 10848 LNCS(1), 557-566. https://doi.org/10.1007/978-3-319-93031-2_40 (Original work published 2018)