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An evolutionary approach for tuning parametric Esau and Williams heuristics

An evolutionary approach for tuning parametric Esau and Williams heuristics
An evolutionary approach for tuning parametric Esau and Williams heuristics
Owing to its inherent difficulty, many heuristic solution methods have been proposed for the capacitated minimum spanning tree problem. On the basis of recent developments, it is clear that the best metaheuristic implementations outperform classical heuristics. Unfortunately, they require long computing times and may not be very easy to implement, which explains the popularity of the Esau and Williams heuristic in practice, and the motivation behind its enhancements. Some of these enhancements involve parameters and their accuracy becomes nearly competitive with the best metaheuristics when they are tuned properly, which is usually done using a grid search within given search intervals for the parameters. In this work, we propose a genetic algorithm parameter setting procedure. Computational results show that the new method is even more accurate than an enumerative approach, and much more efficient
0160-5682
Battarra, M
4cd10bdf-8b57-4bb3-b6d6-a340180963b6
Oncan, T.
92eb08f9-2a85-4eca-bebb-1b5503bea0cc
Altinel, I. K.
9b217aea-872f-4554-9bc1-392b6f34b2e5
Golden, B.
0aae65fa-3594-412e-9a53-5f061ab8897f
Vigo, D.
26943142-a4ee-45d2-84f5-e8f672e2a33f
Phillips, E.
16ee48e2-0cca-478b-b233-28aca3957406
Battarra, M
4cd10bdf-8b57-4bb3-b6d6-a340180963b6
Oncan, T.
92eb08f9-2a85-4eca-bebb-1b5503bea0cc
Altinel, I. K.
9b217aea-872f-4554-9bc1-392b6f34b2e5
Golden, B.
0aae65fa-3594-412e-9a53-5f061ab8897f
Vigo, D.
26943142-a4ee-45d2-84f5-e8f672e2a33f
Phillips, E.
16ee48e2-0cca-478b-b233-28aca3957406

Battarra, M, Oncan, T. and Altinel, I. K. et al. (2011) An evolutionary approach for tuning parametric Esau and Williams heuristics. Journal of the Operational Research Society. (doi:10.1057/jors.2011.36).

Record type: Article

Abstract

Owing to its inherent difficulty, many heuristic solution methods have been proposed for the capacitated minimum spanning tree problem. On the basis of recent developments, it is clear that the best metaheuristic implementations outperform classical heuristics. Unfortunately, they require long computing times and may not be very easy to implement, which explains the popularity of the Esau and Williams heuristic in practice, and the motivation behind its enhancements. Some of these enhancements involve parameters and their accuracy becomes nearly competitive with the best metaheuristics when they are tuned properly, which is usually done using a grid search within given search intervals for the parameters. In this work, we propose a genetic algorithm parameter setting procedure. Computational results show that the new method is even more accurate than an enumerative approach, and much more efficient

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e-pub ahead of print date: 1 June 2011
Organisations: Operational Research

Identifiers

Local EPrints ID: 204843
URI: https://eprints.soton.ac.uk/id/eprint/204843
ISSN: 0160-5682
PURE UUID: 1659ebf8-a984-48b1-936f-32a9684139be

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Date deposited: 02 Dec 2011 11:28
Last modified: 16 Jul 2019 23:17

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Contributors

Author: M Battarra
Author: T. Oncan
Author: I. K. Altinel
Author: B. Golden
Author: D. Vigo
Author: E. Phillips

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