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Randomized heuristics for the Capacitated Clustering Problem

Martinez-Gavara, Anna; Landa-Silva, Dario; Campos, Vicente; Marti, Rafael

Authors

Anna Martinez-Gavara

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DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation

Vicente Campos

Rafael Marti



Abstract

In this paper, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomization and greediness on the performance of these multi-start heuristic search methods when solving this NP-hard problem. The former is a memory-less approach that constructs independent solutions, while the latter is a memory-based method that constructs linked solutions, obtained by partially rebuilding previous ones. Both are based on the combination of greediness and randomization in the constructive process, and coupled with a subsequent local search phase. We propose these two multi-start methods and their hybridization and compare their performance on the CCP. Additionally, we propose a heuristic based on the mathematical programming formulation of this problem, which constitutes a so-called matheuristic. We also implement a classical randomized method based on simulated annealing to complete the picture of randomized heuristics. Our extensive experimentation reveals that Iterated Greedy performs better than GRASP in this problem, and improved outcomes are obtained when both methods are hybridized and coupled with the matheuristic. In fact, the hybridization is able to outperform the best approaches previously published for the CCP. This study shows that memory-based construction is an effective mechanism within multi-start heuristic search techniques.

Citation

Martinez-Gavara, A., Landa-Silva, D., Campos, V., & Marti, R. (2017). Randomized heuristics for the Capacitated Clustering Problem. Information Sciences, 417, https://doi.org/10.1016/j.ins.2017.06.041

Journal Article Type Article
Acceptance Date Jun 28, 2017
Online Publication Date Jul 1, 2017
Publication Date Nov 1, 2017
Deposit Date Aug 10, 2017
Publicly Available Date Mar 29, 2024
Journal Information Sciences
Print ISSN 0020-0255
Electronic ISSN 1872-6291
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 417
DOI https://doi.org/10.1016/j.ins.2017.06.041
Keywords Capacitated Clustering; Grasp; Matheuristic; Graph partitioning
Public URL https://nottingham-repository.worktribe.com/output/965664
Publisher URL http://www.sciencedirect.com/science/article/pii/S002002551631725X?via%3Dihub
Related Public URLs http://www.cs.nott.ac.uk/~pszjds/research/files/dls_is2017.pdf

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