Flow shop scheduling using clustered differential evolution

Davendra, Donald David; Zelinka, Ivan; Onwubolu, Godfrey

dc.title	Flow shop scheduling using clustered differential evolution	en
dc.contributor.author	Davendra, Donald David
dc.contributor.author	Zelinka, Ivan
dc.contributor.author	Onwubolu, Godfrey
dc.relation.ispartof	23rd European Conference on Modelling and Simulation (ECMS 2009)
dc.identifier.isbn	978-0-9553018-8-9
dc.date.issued	2009
dc.citation.spage	70
dc.citation.epage	76
dc.event.title	23rd European Conference on Modelling and Simulation (ECMS)
dc.event.location	Madrid
utb.event.state-en	Spain
utb.event.state-cs	Španělsko
dc.event.sdate	2009-06-09
dc.event.edate	2009-06-12
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	European Council for Modelling and Simulation (ECMS)	en
dc.relation.uri	http://www.scs-europe.net/conf/ecms2009/ecms2009%20CD/ecms2009%20accepted%20papers/is_0028_bce3df7d.pdf
dc.subject	Differential evolution	en
dc.subject	flow shop scheduling	en
dc.subject	clustering	en
dc.description.abstract	A generic approach of population dynamics and clustering for permutative problems is presented in this paper. Diversity indicators are created from solution ordering and its mapping is shown as an advantage for population control in metaheuristics. Differential Evolution Algorithm is embedded with a population based on this approach and vetted with the Flow shop scheduling problem. Extensive experimentation is conducted on benchmark problems in this area with good results.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1002792
utb.identifier.rivid	RIV/70883521:28140/09:63510075!RIV11-MSM-28140___
utb.identifier.obdid	43861054
utb.identifier.scopus	2-s2.0-84857722324
utb.identifier.wok	000302016000011
utb.source	d-wok
dc.date.accessioned	2012-06-04T11:34:08Z
dc.date.available	2012-06-04T11:34:08Z
utb.contributor.internalauthor	Davendra, Donald David
utb.contributor.internalauthor	Zelinka, Ivan