Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage...

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Main Authors: Pierre, D.M., Zakaria, N.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.19582 /
Published: Elsevier Ltd 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992126241&doi=10.1016%2fj.asoc.2016.09.039&partnerID=40&md5=6c6a5901c8044018dedbf0df784b2b2d
http://eprints.utp.edu.my/19582/
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spelling utp-eprints.195822018-04-20T07:11:19Z Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows Pierre, D.M. Zakaria, N. This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator. © 2016 Elsevier B.V. Elsevier Ltd 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992126241&doi=10.1016%2fj.asoc.2016.09.039&partnerID=40&md5=6c6a5901c8044018dedbf0df784b2b2d Pierre, D.M. and Zakaria, N. (2017) Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows. Applied Soft Computing Journal, 52 . pp. 863-876. http://eprints.utp.edu.my/19582/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator. © 2016 Elsevier B.V.
format Article
author Pierre, D.M.
Zakaria, N.
spellingShingle Pierre, D.M.
Zakaria, N.
Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
author_sort Pierre, D.M.
title Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
title_short Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
title_full Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
title_fullStr Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
title_full_unstemmed Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
title_sort stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows
publisher Elsevier Ltd
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992126241&doi=10.1016%2fj.asoc.2016.09.039&partnerID=40&md5=6c6a5901c8044018dedbf0df784b2b2d
http://eprints.utp.edu.my/19582/
_version_ 1741196230031048704
score 11.62408

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