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Optimal Elevator Group Control by Evolution Strategies
Thomas Beielstein1, Claus-Peter Ewald2, and Sandor Markon3
1Universtität Dortmund
D-44221 Dortmund, Germany
Thomas.Beielstein@udo.edu
http://ls11-www.cs.uni-dortmund.de/people/tom/index.html
2NuTech Solutions GmbH
Martin-Schmeisser Weg 15
D-44227 Dortmund, Germany
Ewald@nutechsolutions.de
http://www.nutechsolutions.de
3FUJITEC Co.Ltd. World Headquarters
28-10, Shoh 1-chome
Osaka, Japan
markon@rd.fujitec.co.jp
http://www.fujitec.com
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.
LNCS 2724, p. 1963 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003