1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Άρθρα/Articles
Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30833
DC FieldValueLanguage
dc.contributor.authorDIao, Yiya-
dc.contributor.authorLi, Changhe-
dc.contributor.authorZeng, Sanyou-
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorYang, Shengxiang-
dc.date.accessioned2023-11-21T13:00:02Z-
dc.date.available2023-11-21T13:00:02Z-
dc.date.issued2019-06-10-
dc.identifier.citation2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10 - 13 June 2019en_US
dc.identifier.isbn9781728121536-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30833-
dc.description.abstractThis paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based multi-population strategy with a memory scheme, namely, multi-population GL (MPGL). The framework is instantiated for a 3D dynamic shortest path problem, which is developed in this paper. Experimental comparison studies show that MPGL is able to quickly adapt to new environments and it outperforms several ant colony optimization variants.en_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.subjectant colony optimizationen_US
dc.subjectclusteringbased multi-populationen_US
dc.subjectdynamic sequence optimizationen_US
dc.subjectdynamic shortest pathen_US
dc.subjectgenetic learningen_US
dc.titleMemory-based multi-population genetic learning for dynamic shortest path problemsen_US
dc.typeConference Papersen_US
dc.collaborationChina University of Geosciencesen_US
dc.collaborationHubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systemsen_US
dc.collaborationChina University of Geosciencesen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationDe Montfort Universityen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryCyprusen_US
dc.countryChinaen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.relation.conference2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedingsen_US
dc.identifier.doi10.1109/CEC.2019.8790211en_US
dc.identifier.scopus2-s2.0-85071308393en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85071308393en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyear2019-2020en_US
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0002-5281-4175-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

SCOPUSTM   
Citations 20

5
checked on Mar 14, 2024

Page view(s)

100
Last Week
0
Last month
1
checked on Nov 8, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.