Authors: Brimberg, Jack
Mladenović, Nenad 
Todosijević, Raca 
Urošević, Dragan 
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Solving the capacitated clustering problem with variable neighborhood search
Journal: Annals of Operations Research
Volume: 272
Issue: 1-2
First page: 289
Last page: 321
Issue Date: 1-Jan-2019
Rank: M22
ISSN: 0254-5330
DOI: 10.1007/s10479-017-2601-5
Variable neighborhood search (VNS) is a proven heuristic framework for finding good solutions to combinatorial and global optimization problems. In this paper two VNS-based heuristics are proposed for solving the capacitated clustering problem. The first follows a standard VNS approach, and the second a skewed VNS that allows moves to inferior solutions. The performance of the two heuristics is assessed on benchmark instances from the literature. We also compare their performance against a recently published iterated VNS procedure. All VNS procedures outperform the state-of-the-art, but the Skewed VNS is best overall. This would suggest that using acceptance criteria before allowing moves to inferior solutions in Skewed VNS is preferable to the random shaking approach that is used in Iterated VNS to move to new regions of the solution space.
Keywords: Clustering | Heuristic | Local search | Optimization
Publisher: Springer Link
Project: Mathematical Modelas and Optimization Methods on Large-Scale Systems 
Development of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education 

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