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dc.contributor.authorWang, Hongfengen
dc.contributor.authorWang, Dingweien
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2017-03-14T15:06:34Z
dc.date.available2017-03-14T15:06:34Z
dc.date.issued2007
dc.identifier.citationWang, H., Wang, D. and Yang, S. (2007) Triggered memory-based swarm optimization in dynamic environments. EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science, 4448, pp. 637-646en
dc.identifier.urihttp://hdl.handle.net/2086/13571
dc.description.abstractIn recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems.en
dc.language.isoen_USen
dc.publisherSpringeren
dc.titleTriggered memory-based swarm optimization in dynamic environmentsen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen


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