The University of Southampton
University of Southampton Institutional Repository

Tuning a parametric Clarke–Wright heuristic via a genetic algorithm

Tuning a parametric Clarke–Wright heuristic via a genetic algorithm
Tuning a parametric Clarke–Wright heuristic via a genetic algorithm
Almost all heuristic optimization procedures require the presence of a well-tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter-setting approach previously proposed in the literature. The results of our computational testing show that our new parameter-setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time
vehicle routing, heuristics, genetic algorithms
0160-5682
1568-1572
Battarra, Maria
0498dc58-e9d5-4ad2-a141-040f7bcebbc2
Golden, Bruce
971bcae2-be5b-4afd-acc0-3b558d31f7d2
Vigo, Daniele
0bc6db04-0bff-438e-91ca-947171d0604e
Battarra, Maria
0498dc58-e9d5-4ad2-a141-040f7bcebbc2
Golden, Bruce
971bcae2-be5b-4afd-acc0-3b558d31f7d2
Vigo, Daniele
0bc6db04-0bff-438e-91ca-947171d0604e

Battarra, Maria, Golden, Bruce and Vigo, Daniele (2008) Tuning a parametric Clarke–Wright heuristic via a genetic algorithm. Journal of the Operational Research Society, 59, 1568-1572. (doi:10.1057/palgrave.jors.2602488).

Record type: Article

Abstract

Almost all heuristic optimization procedures require the presence of a well-tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter-setting approach previously proposed in the literature. The results of our computational testing show that our new parameter-setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time

Full text not available from this repository.

More information

e-pub ahead of print date: 29 August 2007
Published date: 2008
Keywords: vehicle routing, heuristics, genetic algorithms
Organisations: Operational Research

Identifiers

Local EPrints ID: 204849
URI: https://eprints.soton.ac.uk/id/eprint/204849
ISSN: 0160-5682
PURE UUID: 48ea6bbf-c723-4b55-a16d-b0762171b646

Catalogue record

Date deposited: 02 Dec 2011 14:13
Last modified: 18 Jul 2017 11:05

Export record

Altmetrics

Contributors

Author: Maria Battarra
Author: Bruce Golden
Author: Daniele Vigo

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×