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Comparison of differential evolution and SOMA in the task of chaos control optimization - Extended study: Complex target CF

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dc.title Comparison of differential evolution and SOMA in the task of chaos control optimization - Extended study: Complex target CF en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Zelinka, Ivan
dc.contributor.author Oplatková, Zuzana
dc.relation.ispartof 2009 IEEE Congress on Evolutionary Computation, Vols 1-5
dc.identifier.isbn 978-1-4244-2958-5
dc.date.issued 2009
dc.citation.spage 2825
dc.citation.epage 2832
dc.event.title IEEE Congress on Evolutionary Computation
dc.event.location Trondheim
utb.event.state-en Norway
utb.event.state-cs Norsko
dc.event.sdate 2009-05-18
dc.event.edate 2009-05-21
dc.type conferenceObject
dc.language.iso en
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en
dc.identifier.doi 10.1109/CEC.2009.4983297
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4983297
dc.description.abstract This work deals with the comparison of performance of two selected evolutionary algorithms (EA) in the task of optimization of the control of chaos. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control, leading to satisfactory results and to show extreme sensitivity of quality of results on the selection of EA, setting-up of EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two dimensional Henon map was used. Two complex targeting cost functions were tested in this work. The optimization was realized in several ways, each one for another evolutionary algorithm or another desired periodic orbit and behavior of system. The evolutionary algorithms, SOMA (Self-Organizing Migrating Algorithm) and DE (Differential Evolution) were used in several versions. For each version, repeated simulations demonstrated the robustness of the used method and constructed CF. Finally, the obtained results are compared. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1001848
utb.identifier.rivid RIV/70883521:28140/11:43866531!RIV12-MSM-28140___
utb.identifier.obdid 43866032
utb.identifier.scopus 2-s2.0-70449922032
utb.identifier.wok 000274803101110
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:05Z
dc.date.available 2011-08-09T07:34:05Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Zelinka, Ivan
utb.contributor.internalauthor Oplatková, Zuzana
utb.fulltext.affiliation Roman Senkerik, Ivan Zelinka, Zuzana Oplatkova Roman Senkerik, Ivan Zelinka and Zuzana Oplatkova are with the Department of Applied Informatics, Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 762 72 Zlín, Czech Republic; e-mail: {senkerik, zelinka, oplatkova}@fai.utb.cz. Phone: +420 57 603 5189
utb.fulltext.dates Manuscript received November 14, 2008.
utb.fulltext.references [1] Just W., “Principles of Time Delayed Feedback Control”, In: Schuster H.G., Handbook of Chaos Control, Wiley-Vch, ISBN 3-527-29436-8, 1999. [2] Pyragas K., “Continuous control of chaos by self-controlling feedback”, Physics Letters A, 170, 1992, 421-428. [3] Ott E., C. Greboki, J.A. Yorke, “Controlling Chaos”, Phys. Rev. Lett. vol. 64, 1990, pp. 1196-1199. [4] Richter H. and K. J. Reinschke, “Optimization of local control of chaos by an evolutionary algorithm”, Physica D, vol. 144, 2000, pp. 309-334. [5] Richter H., An Evolutionary “Algorithm for Controlling Chaos: The Use of Multi - Objective Fitness Function”, Lecture Notes in Computer Science, vol. 2439, 2002, pp. 308-320. [6] Pyragas K., “Control of chaos via extended delay feedback”, Physics Letters A, vol. 206, 1995, pp. 323-330. [7] Zelinka I., Senkerik R., Navratil E., “Investigation on Evolutionary Optimitazion of Chaos Control”, Chaos, Solitons & Fractals (2007), doi:10.1016/j.chaos.2007.07.045 [8] Zelinka I., Senkerik R., Navratil E., “Investigation on Real Time Deterministic Chaos Control by Means of Evolutionary Algorithms”, In Proc. 1st IFAC Conference on Analysis and Control of Chaotic Systems, Reims, France, 28-30 June 2006, pages 211-217. [9] Senkerik R., Zelinka I., Navratil E., “Optimization of feedback control of chaos by evolutionary algorithms”, in proc 1st IFAC Conference on analysis and control of chaotic systems, Reims, France, 2006, In Press. [10] Senkerik R., Zelinka I., Davendra D., “Comparison of Evolutionary Algorithms in the Task of Chaos Control Optimization”, CEC’07, In Proc. IEEE Congres on Evolutionary Computation 2007, Singapore, Singapore, 25-28 September 2007, pages 3952-3958 [11] Zelinka I., “SOMA – Self Organizing Migrating Algorithm”, In: New Optimization Techniques in Engineering, (B.V. Babu, G. Onwubolu (eds)), chapter 7, 33, Springer-Verlag,. [12] Price K, “An Introduction to Differential Evolution”, In: New Ideas in Optimization, (D. Corne, M. Dorigo and F. Glover, Eds.), p. 79–108, McGraw-Hill, London, UK, ISBN 007-709506-5, 1999.
utb.fulltext.sponsorship This work was supported by the grant NO. MSM 7088352101 of the Ministry of Education of the Czech Republic and by grants of Grant Agency of Czech Republic GACR 102/06/1132.
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