Randomized heuristics for the Capacitated Clustering Problem

Martinez-Gavara, Anna and Landa-Silva, Dario and Campos, Vicente and Marti, Rafael (2017) Randomized heuristics for the Capacitated Clustering Problem. Information Sciences, 417 . pp. 154-168. ISSN 1872-6291

[img] PDF - Repository staff only until 1 July 2018. - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution Non-commercial No Derivatives.
Download (1MB)


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.

Item Type: Article
Keywords: Capacitated Clustering; Grasp; Matheuristic; Graph partitioning
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1016/j.ins.2017.06.041
Related URLs:
Depositing User: Landa-Silva, Dario
Date Deposited: 10 Aug 2017 14:16
Last Modified: 11 Aug 2017 03:35
URI: http://eprints.nottingham.ac.uk/id/eprint/44825

Actions (Archive Staff Only)

Edit View Edit View