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Cluster restarted DM: New algorithm for global optimisation

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dc.title Cluster restarted DM: New algorithm for global optimisation en
dc.contributor.author Dlapa, Marek
dc.relation.ispartof 2017 Intelligent Systems Conference, IntelliSys 2017
dc.identifier.isbn 978-1-5090-6435-9
dc.date.issued 2018
utb.relation.volume 2018-January
dc.citation.spage 1130
dc.citation.epage 1135
dc.event.title 2017 Intelligent Systems Conference, IntelliSys 2017
dc.event.location London
utb.event.state-en United Kingdom
utb.event.state-cs Spojené království
dc.event.sdate 2017-09-07
dc.event.edate 2017-09-08
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/IntelliSys.2017.8324271
dc.relation.uri https://ieeexplore.ieee.org/document/8324271/
dc.subject global optimisation en
dc.subject evolutionary algorithms en
dc.subject covariance matrix adaptation en
dc.subject self-organizing migration algorithm en
dc.description.abstract Global optimisation method Differential Migration (DM) with restarting is described in this paper and evaluated together with Restart Covariance Matrix Adaptation Evolution Strategy With Increasing Population Size (IPOP-CMA-ES). Differential Migration is another step in global optimisation from SOMA (Self-Organizing Migration Algorithm) combining two basic individual movement methods of SOMA - all-to-one and all-to-all, via cluster analysis and internal algorithm constant defining continuous change from one type of movement to another. The proposed algorithm implements essential ideas of Differential Evolution regardless of their original interpretation in living nature with subsequent increase of efficiency in finding global extreme which holds mainly for noisy multimodal cost functions present in the benchmarks as well as in real world applications. © 2017 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008157
utb.identifier.obdid 43876946
utb.identifier.scopus 2-s2.0-85050889335
utb.identifier.wok 000456827800152
utb.source d-scopus
dc.date.accessioned 2018-08-29T08:26:57Z
dc.date.available 2018-08-29T08:26:57Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic [LO1303 (MSM - 7778//2014)]
utb.ou CEBIA-Tech
utb.contributor.internalauthor Dlapa, Marek
utb.fulltext.affiliation Marek Dlapa Faculty of Applied Informatics Tomas Bata University in Zlin Nad Stranemi 4511, 760 05 Zlin, Czech Rep. E-mail: dlapa@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references [1] Auger and N. Hansen, “Performance Evaluation of an Advanced Local Search Evolutionary Algorithm,” In Proceedings of the IEEE Congress on Evolutionary Computation, 2005, pp.1777-1784. [2] Auger and N. Hansen, “A Restart CMA Evolution Strategy With Increasing Population Size,” In Proceedings of the IEEE Congress on Evolutionary Computation, 2005, pp. 1769-1776. [3] D.V. Arnold and H.G. Beyer, “Performance analysis of evolutionary optimization with cumulative step length adaptation,” IEEE Transactions on Automatic Control, 49(4):617–622, 2004. [4] H.G. Beyer and D.V. Arnold, “Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies,” Evolutionary Computation, 11(1):19–28, 2003. [5] M. Dlapa, R. Prokop, and I. Zelinka, “Evolutionary algorithms in fuzzy logic control,” Proceedings of Process Control, 2001. [6] M. Dlapa and R., Prokop, “Design and analysis of simple robust controllers via structured singular value,” Int. J. Automation and Control, Vol. 2, No. 1, pp. 73-89, ISSN (Online) 1740-7524, ISSN (Print) 1740-7516, 2008. [7] M. Dlapa and R. Prokop, “Algebraic approach to controller design using structured singular value,” Control Engineering Practice, Vol. 18, No. 4, April 2010, ISSN 0967-0661. [8] M. Dlapa, “Differential Migration: Sensitivity Analysis and Comparison Study,” In 2009 IEEE Congress on Evolutionary Computation (IEEE CEC 2009), Trondheim, Norway, 2009, pp. 1729-1736, ISBN 978-1-4244-2959-2. [9] M. Dlapa, “Controller Design for a Two Tank System Using Structured Singular Value and Direct Search Methods,” 18th World Congress of the International Federation of Automatic Control (IFAC), August 28 - September 2 2011, Milan, Italy, pp. 7511-7516, ISSN: 1474-6670. [10] S. Fujita and H. Nishimura, “An Evolutionary Approach to Associative Memory in Recurrent Neural Networks,” Neural Process., Vol. 1, No. 2, 1994, pp. 9-13. [11] D.B. Fogel, “An Introduction to Simulated Evolutionary Optimization,” IEEE Trans. on Neural Networks., Vol. 5, No. 1, January 1994, pp. 3-14. [12] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. [13] N. Hansen and S. Kern, “Evaluating the CMA evolution strategy on multimodal test functions,” In Xin Yao et al., editors, Parallel Problem Solving from Nature - PPSN VIII, pages 282–291. Springer, 2004. [14] N. Hansen, “Benchmarking a BI-Population CMA-ES on the BBOB2009 Function Testbed,” In the workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, pages 2389–2395. ACM, 2009. [15] Internet: http://dlapa.cz/homeeng.htm [16] Internet: http://www.icsi.berkeley.edu/~storn/code.html [17] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. IEEE int'l conf. on neural networks Vol. IV, IEEE service center, Piscataway, NJ, 1995, pp. 1942-1948. [18] R. Ros and N. Hansen, “A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity,” In Rudolph et al. (eds.) Parallel Problem Solving from Nature, PPSN X, Proceedings, pp. 296-305, Springer, 2008. [19] R. Storn and K. Price. “Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces,” Journal of Global Optimization, Vol. 11, 1997, pp. 341–359. [20] R. Storn, “System design by constraints adaptation and Differential Evolution,” IEEE Trans. on Evolutionary Computation, Vol. 3, No. 1, 1999, pp. 22-23. [21] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.- P. Chen, A. Auger, and S. Tiwari. Problem de¿nitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore, May 2005. http://www.ntu.edu.sg/home/EPNSugan. [22] Zelinka, Chapter 7, “SOMA - Self Organizing Migrating Algorithm,” In: G. Onwubolu – B.V. Babu, New Optimization Techniques in Engineering, Springer-Verlag, 2004.
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSM-7778//2014).
utb.wos.affiliation [Dlapa, Marek] Tomas Bata Univ Zlin, Fac Appl Informat, Nad Stranemi 4511, Zlin 76005, Czech Republic
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin, Czech Republic
utb.fulltext.projects LO1303 (MSM-7778//2014)
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