Optimal Elevator Group Control by Evolution Strategies

dc.contributor.author	Beielstein, Thomas	de
dc.contributor.author	Ewald, Claus-Peter	de
dc.contributor.author	Markon, Sandor	de
dc.date.accessioned	2004-12-07T08:21:25Z
dc.date.available	2004-12-07T08:21:25Z
dc.date.created	2003	de
dc.date.issued	2003-12-23	de
dc.description.abstract	Efficient elevator group control is important for the operation of large buildings. Recent developments in this field include the use of fuzzy logic and neural networks. This paper summarizes the development of an evolution strategy (ES) that is capable of optimizing the neuro-controller of an elevator group controller. It extends the results that were based on a simplified elevator group controller simulator. A threshold selection technique is presented as a method to cope with noisy fitness function values during the optimization run. Experimental design techniques are used to analyze first experimental results.	en
dc.format.extent	502784 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/5436
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15353
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 146	de
dc.subject.ddc	004	de
dc.title	Optimal Elevator Group Control by Evolution Strategies	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

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