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A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic

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Abstract

Adaptive large neighborhood search (ALNS) is a useful framework for solving difficult combinatorial optimisation problems. As a metaheuristic, it consists of some components that must be tailored to the specific optimisation problem that is being solved, while other components are problem independent. The literature is sparse with respect to studies that aim to evaluate the relative merit of different alternatives for specific problem independent components. This paper investigates one such component, the move acceptance criterion in ALNS, and compares a range of alternatives. Through extensive computational testing, the alternative move acceptance criteria are ranked in three groups, depending on the performance of the resulting ALNS implementations. Among the best variants, we find versions of criteria based on simulated annealing, threshold acceptance, and record-to-record travel, with a version of the latter being consistently undominated by the others. Additional analyses focus on the search behavior, and multiple linear regression is used to identify characteristics of search behavior that are associated with good search performance.

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Acknowledgements

The authors thank two anonymous referees for their helpful comments that led to several improvements of the original manuscript.

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Authors and Affiliations

  1. Pompeu Fabra University and Barcelona GSE, Carrer Ramon Trias-Fargas 25, 08005, Barcelona, Spain

    Alberto Santini

  2. DTU Management Science, Produktionstorvet 424, 2800 Kgs., Lyngby, Denmark

    Stefan Ropke

  3. Molde University College, Britvegen 2, 6410, Molde, Norway

    Lars Magnus Hvattum

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  1. Alberto Santini
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  2. Stefan Ropke
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  3. Lars Magnus Hvattum
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Correspondence to Alberto Santini.

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Santini, A., Ropke, S. & Hvattum, L.M. A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic. J Heuristics 24, 783–815 (2018). https://doi.org/10.1007/s10732-018-9377-x

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  • DOI: https://doi.org/10.1007/s10732-018-9377-x

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