Partially Optimized Cyclic Shift Crossover for Multi-Objective Genetic Algorithms for the multi-objective Vehicle Routing Problem with time-windows

The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with Multi-Objective Genetic Algorithms (MOGAs). MOGAs, thanks to its genetic ope...

Full description

Saved in:
Bibliographic Details
Published in:2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) pp. 106 - 115
Main Authors: Pierre, Djamalladine Mahamat, Zakaria, Nordin
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2014
Subjects:
Biological cells
Crossover
Genetic algorithms
Multi-objective Genetic Algorithm
Multi-objective Vehicle Routing Problem
Mutation
Optimization
Sociology
Statistics
Vehicles
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with Multi-Objective Genetic Algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.
AbstractList The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of optimization. Recently, attentions have turned to multi-objective VRPs with Multi-Objective Genetic Algorithms (MOGAs). MOGAs, thanks to its genetic operators such as selection, crossover, and/or mutation, constantly modify a population of solutions in order to find optimal solutions. However, given the complexity of VRPs, conventional crossover operators have major drawbacks. The Best Cost Route Crossover is lately gaining popularity in solving multi-objective VRPs. It employs a brute force approach to generate new children. Such approach may be unacceptable when presented with a relatively large problem instance. In this paper, we introduce a new crossover operator, called Partially Optimized Cyclic Shift Crossover (POCSX). A comparative study, between a MOGA based on POCSX, and a MOGA which is based on the Best Cost Route Crossover affirms the level of competitiveness of the former.
Author Pierre, Djamalladine Mahamat
Zakaria, Nordin
Author_xml – sequence: 1
  givenname: Djamalladine Mahamat
  surname: Pierre
  fullname: Pierre, Djamalladine Mahamat
  email: djamal2810@gmail.com
  organization: High Performance Comput. Center, Univ. Teknol. PETRONAS, Tronoh, Malaysia
– sequence: 2
  givenname: Nordin
  surname: Zakaria
  fullname: Zakaria, Nordin
  email: nordinzakaria@gmail.com
  organization: High Performance Comput. Center, Univ. Teknol. PETRONAS, Tronoh, Malaysia
BookMark eNo90NFOwjAUBuCa6IWgD2C86QsM23W29JJMBRMIRInxjnTdGTumW0lXIPgavrAEiVfn5vv_Pzk9ctn6Fgi542zAOdMPs_xpNkgZzwaKMcX14wXp8UxpnWVSfV6Tn4UJEY1zBzrfRGzwG0qaH6xDS99rrCLNg-86v4NAKx_obOsiJvPiC2zEHdAxtBCPduTWPmCsm-7EYg20OVH_Tz-gRuuAvvltxHZNF8EXDhq6P8bocRqSPbal33c35KoyroPb8-2T5cvzMp8k0_n4NR9NE9QsJsqkLBWWW10UpZVCGlGWcigrKZTkBkDZLJWF5czoTBVGmhQyrUrOZSqGVSX65P6vFgFgtQnYmHBYnd8kfgG8TWWA
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/MCDM.2014.7007195
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
EISBN 147994467X
9781479944675
EndPage 115
ExternalDocumentID 7007195
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i90t-7a2023c1c9bbdc636a3dd686f63761aee7c426bc10a947ba6a2e497d116238ff3
IEDL.DBID RIE
IngestDate Thu Jun 29 18:37:41 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i90t-7a2023c1c9bbdc636a3dd686f63761aee7c426bc10a947ba6a2e497d116238ff3
PageCount 10
ParticipantIDs ieee_primary_7007195
PublicationCentury 2000
PublicationDate 2014-Dec.
PublicationDateYYYYMMDD 2014-12-01
PublicationDate_xml – month: 12
  year: 2014
  text: 2014-Dec.
PublicationDecade 2010
PublicationTitle 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)
PublicationTitleAbbrev MCDM
PublicationYear 2014
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.5810654
Snippet The complexity of the Vehicle Routing Problems (VRPs) and their applications in our day to day life has garnered a lot of attentions in the area of...
SourceID ieee
SourceType Publisher
StartPage 106
SubjectTerms Biological cells
Crossover
Genetic algorithms
Multi-objective Genetic Algorithm
Multi-objective Vehicle Routing Problem
Mutation
Optimization
Sociology
Statistics
Vehicles
Title Partially Optimized Cyclic Shift Crossover for Multi-Objective Genetic Algorithms for the multi-objective Vehicle Routing Problem with time-windows
URI https://ieeexplore.ieee.org/document/7007195
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI62icNOPDbEWzlwJFu7lqY5osHEZQ-JCe02pYnLisaKto5p_A3-MHZbDSFx4VZVURvZcT47-Wwzdt0BjUDkWhFaxxN-ZNCkOioWjtKeglAiBuu82YQcDMLJRI0q7GaXCwMAOfkMWvSY3-Xb1KzpqKwtCRDVbZVVpQyKXK3yotJ1VLvfve8TV8tvleN-NUzJ8aK3_78_HbDmT-IdH-0g5ZBVYHHE6uQQFvWUG-xrRLrW8_mWD9Ha35JPsLy7NfPE8KdZEme8S7hHxEyO_ijPE2zFMHotNjZOZabxS_xu_pIuk2z2tsqHoRvIc26hSHdDn2FGi4oTaQhnQxOj3jOcjm459aQXGwzo082qyca9h3H3UZSNFUSinExITT3TjWtUFFkTeIH2rA3CIA5wt3E1gDSI25FxHa18GelAd8BX0rou-kphHHvHrLZIF3DCuIo8cDyMsTwd-oDRlgU08BCM9k2A7sMpa5Bwp-9F6YxpKdezv1-fszrpr2CLXLBatlzDJdszHyjk5VWu72_DGbGD
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI7GQGInHgPxJgeOZGvXrm2OaDANsZfEhHab0sRlRduK9mAaf4M_jN1OQ0hcuFVV1EZ2nM9OPtuM3VRAIRDZRgTGcoQbajSpioyEJZUjIfARg1XabMJvt4N-X3Zz7HaTCwMAKfkMSvSY3uWbRC_oqKzsEyDK6hbbrrpuxcqytdZXlbYly63afYvYWm5pPfJXy5QUMep7__vXPjv6Sb3j3Q2oHLAcTA5ZgVzCrKJykX11SdtqNFrxDtr7OP4Ew2srPYo1fx7G0ZzXCPmImsnRI-Vpiq3ohG_Z1sap0DR-id-NXpNpPB-OZ-kwdAR5yi4UyWboCwxpWXGiDeFsaGLUfYbT4S2nrvRiiSF9spwdsV79oVdriHVrBRFLay58RV3Tta1lGBrtOZ5yjPECL_Jwv7EVgK8RuUNtW0q6fqg8VQFX-sa20VsKosg5ZvlJMoETxmXogOVglOWowAWMtwygiQeglas9dCBOWZGEO3jPimcM1nI9-_v1Ndtt9FrNQfOx_XTOCqTLjDtywfLz6QIu2Y7-QIFPr1LdfwMjp7TK
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2014+IEEE+Symposium+on+Computational+Intelligence+in+Multi-Criteria+Decision-Making+%28MCDM%29&rft.atitle=Partially+Optimized+Cyclic+Shift+Crossover+for+Multi-Objective+Genetic+Algorithms+for+the+multi-objective+Vehicle+Routing+Problem+with+time-windows&rft.au=Pierre%2C+Djamalladine+Mahamat&rft.au=Zakaria%2C+Nordin&rft.date=2014-12-01&rft.pub=IEEE&rft.spage=106&rft.epage=115&rft_id=info:doi/10.1109%2FMCDM.2014.7007195&rft.externalDocID=7007195