Details

Autor(en) / Beteiligte

• Elshaikh, Abdalla
• Salhi, Said
• Brimberg, Jack
• Mladenović, Nenad
• Callaghan, Becky
• Nagy, Gábor

Titel

An adaptive perturbation-based heuristic: An application to the continuous p-centre problem

Ist Teil von

• Computers & operations research, 2016-11, Vol.75, p.1-11

Ort / Verlag

New York: Elsevier Ltd

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 with575nodes 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. •Two new perturbation heuristics are proposed for the continuous p-centre problem.•A novel local search is designed based on covering circles.•A learning process is introduced making the heuristics self-adaptive and effective.•New best results including new optimal solutions are found.