Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30833
DC Field | Value | Language |
---|---|---|
dc.contributor.author | DIao, Yiya | - |
dc.contributor.author | Li, Changhe | - |
dc.contributor.author | Zeng, Sanyou | - |
dc.contributor.author | Mavrovouniotis, Michalis | - |
dc.contributor.author | Yang, Shengxiang | - |
dc.date.accessioned | 2023-11-21T13:00:02Z | - |
dc.date.available | 2023-11-21T13:00:02Z | - |
dc.date.issued | 2019-06-10 | - |
dc.identifier.citation | 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 10 - 13 June 2019 | en_US |
dc.identifier.isbn | 9781728121536 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/30833 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.rights | © IEEE | en_US |
dc.subject | ant colony optimization | en_US |
dc.subject | clusteringbased multi-population | en_US |
dc.subject | dynamic sequence optimization | en_US |
dc.subject | dynamic shortest path | en_US |
dc.subject | genetic learning | en_US |
dc.title | Memory-based multi-population genetic learning for dynamic shortest path problems | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | China University of Geosciences | en_US |
dc.collaboration | Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems | en_US |
dc.collaboration | China University of Geosciences | en_US |
dc.collaboration | University of Cyprus | en_US |
dc.collaboration | De Montfort University | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.country | Cyprus | en_US |
dc.country | China | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.relation.conference | 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings | en_US |
dc.identifier.doi | 10.1109/CEC.2019.8790211 | en_US |
dc.identifier.scopus | 2-s2.0-85071308393 | en |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85071308393 | en |
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.academicyear | 2019-2020 | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
crisitem.author.orcid | 0000-0002-5281-4175 | - |
Appears in Collections: | Άρθρα/Articles |
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