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.
CITATION STYLE
Thomas, C., & Schaus, P. (2018). Revisiting the self-adaptive large neighborhood search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10848 LNCS, pp. 557–566). Springer Verlag. https://doi.org/10.1007/978-3-319-93031-2_40
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