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An Adaptive Perturbation-Based Heuristic: An Application to the Continuous p-Centre Problem

Elshaikh, Abdella, Salhi, Said, Brimberg, Jack, Mladenovic, Nenad, Callaghan, Becky, Nagy, Gábor (2016) An Adaptive Perturbation-Based Heuristic: An Application to the Continuous p-Centre Problem. Computers and Operations Research, 75 . pp. 1-11. ISSN 0305-0548. (doi:10.1016/j.cor.2016.04.018) (KAR id:55688)

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A self-adaptive heuristic that incorporates a variable level of perturbation, a novel local search and a learning mechanism is proposed to solve the p-centre problem in the continuous space. Empirical results, using several large TSP-Lib data sets, some with over 1300 customers with various values of p, show that our proposed heuristic is both effective and efficient. This perturbation metaheuristic compares favourably against the optimal method on small size instances. For larger instances the algorithm outperforms both a multi-start heuristic and a discrete-based optimal approach while performing well against a recent powerful VNS approach. This is a self-adaptive method that can easily be adopted to tackle other combinatorial/global optimisation problems. For benchmarking purposes, the medium size instances with nodes are solved optimally for the first time, though requiring a large amount of computational time. As a by-product of this research, we also report for the first time the optimal solution of the vertex p-centre problem for these TSP-Lib data sets.

Item Type: Article
DOI/Identification number: 10.1016/j.cor.2016.04.018
Uncontrolled keywords: p-centre problem, continuous space, perturbation search, adaptive search, large instances, optimal solutions
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Said Salhi
Date Deposited: 25 May 2016 09:14 UTC
Last Modified: 06 Oct 2021 11:04 UTC
Resource URI: (The current URI for this page, for reference purposes)
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