Journal of Heuristics

, Volume 24, Issue 5, pp 783–815 | Cite as

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

  • Alberto Santini
  • Stefan Ropke
  • Lars Magnus Hvattum


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.


Adaptive large neighbourhood search Simulated annealing Threshold acceptance Record-to-record travel Vehicle routing problem Capacitated minimum spanning tree Quadratic assignment problem 



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


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Pompeu Fabra University and Barcelona GSEBarcelonaSpain
  2. 2.DTU Management ScienceLyngbyDenmark
  3. 3.Molde University CollegeMoldeNorway

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