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 | Abstract: | 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 |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.