Show simple item record

dc.contributor.authorDiao, Yiya
dc.contributor.authorLi, Changhe
dc.contributor.authorZeng, Sanyou
dc.contributor.authorMavrovouniotis, Michalis
dc.contributor.authorYang, Shengxiang
dc.date.accessioned2019-04-10T08:23:55Z
dc.date.available2019-04-10T08:23:55Z
dc.date.issued2019-06
dc.identifier.citationDiao, Y., Li, C., Zeng, S., Mavrovouniotis, M. and Yang, S. (2019) Memory-based multi-population genetic learning for dynamic shortest path problems. Proceedings of the 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand, June 2019.en
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/17706
dc.descriptionThe file attached to this record is the author's final peer reviewed version.en
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
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectDynamic shortest pathen
dc.subjectDynamic sequence optimizationen
dc.subjectGenetic learningen
dc.subjectAnt colony optimizationen
dc.subjectClustering-based multi-populationen
dc.titleMemory-based multi-population genetic learning for dynamic shortest path problemsen
dc.typeConferenceen
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectid61673355en
dc.cclicenceN/Aen
dc.date.acceptance2019-03
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNational Natural Science Foundation of Chinaen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record