TBU Publications
Repository of TBU Publications

How chaotic sequences and generator sequencing affect the particle trajectory in PSO

DSpace Repository

Show simple item record


dc.title How chaotic sequences and generator sequencing affect the particle trajectory in PSO en
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Kadavý, Tomáš
dc.relation.ispartof 2017 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.identifier.isbn 978-1-5386-2725-9
dc.date.issued 2017
utb.relation.volume 2018-January
dc.citation.spage 1
dc.citation.epage 8
dc.event.title IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
dc.event.location Honolulu
utb.event.state-en Hawaii
utb.event.state-cs Havaj
dc.event.sdate 2017-11-27
dc.event.edate 2017-12-01
dc.type conferenceObject
dc.language.iso en
dc.publisher IEEE
dc.identifier.doi 10.1109/SSCI.2017.8285422
dc.relation.uri https://ieeexplore.ieee.org/abstract/document/8285422/
dc.subject Particle swarm en
dc.subject chaos en
dc.subject PSO en
dc.subject sequencing en
dc.subject number generator en
dc.description.abstract As chaotic sequences became more popular within the evolutionary computation community; we choose to address some of the issues that occur when the extremely sensitive chaotic systems are embedded into the Particle Swarm Optimization algorithm. We investigate here the particle trajectory change with changing chaotic systems and with different sequencing. This research highlights several significant trends through the 2D graphical visualizations and 10D statistics, and further, it provides advice for future studies in this area of hybridizing of complex dynamics and swarm based algorithms. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007912
utb.identifier.rivid RIV/70883521:28140/17:63517161!RIV18-GA0-28140___
utb.identifier.obdid 43877151
utb.identifier.scopus 2-s2.0-85046135961
utb.identifier.wok 000428251402079
utb.source d-wok
dc.date.accessioned 2018-05-18T15:12:07Z
dc.date.available 2018-05-18T15:12:07Z
dc.description.sponsorship Grant Agency of the Czech Republic GACR [P103/15/06700S]; Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2017/004]
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.affiliation Michal Pluhacek, Roman Senkerik, Adam Viktorin, Tomas Kadavy Faculty of Applied Informatics Tomas Bata University in Zlín Nad Stranemi 4511, 760 05 Zlín, Czech Republic {pluhacek,senkerik,aviktorin,kadavy}@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references [1] R. Caponetto, L. Fortuna, S. Fazzino, M.G. Xibilia, Chaotic sequences to improve the performance of evolutionary algorithms, Evolutionary Computation, IEEE Transactions on, vol.7, no.3, pp. 289- 304, June 2003 [2] B. Alatas, E. Akin, B. A. Ozer, Chaos embedded particle swarm optimization algorithms, Chaos, Solitons & Fractals, Volume 40, Issue 4, 30 May 2009, Pages 1715-1734, ISSN 0960-0779. [3] M. Pluhacek, R. Senkerik, and I. Zelinka, I. Particle swarm optimization algorithm driven by multichaotic number generator. Soft Computing, 18(4), 2014, p. 631-639. [4] M. Pluhacek, R. Senkerik, and D. Davendra, Chaos particle swarm optimization with Eensemble of chaotic systems. Swarm and Evolutionary Computation, 2015, 25, p. 29-35. [5] E. Araujo, L. Coelho, Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal- vacuum system, Applied Soft Computing, v.8 n.4, September, 2008 p.1354-1364. [6] D. Davendra, I. Zelinka, R. Senkerik, Chaos driven evolutionary algorithms for the task of PID control, Computers & Mathematics with Applications, Volume 60, Issue 4, 2010, pp 1088-1104, ISSN 0898-1221 [7] M. Pluhacek, R. Senkerik, I. Zelinka and D. Davendra, "Designing PID Controllers by Means of PSO Algorithm Enhanced by Various Chaotic Maps," 2013 8th EUROSIM Congress on Modelling and Simulation, Cardiff, 2013, pp. 19-23. doi: 10.1109/EUROSIM.2013.14 [8] D. C. Huynh and N. Nair, "Chaos PSO algorithm based economic dispatch of hybrid power systems including solar and wind energy sources," 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Bangkok, 2015, pp. 1-6. doi: 10.1109/ISGT-Asia.2015.7386974 [9] I. Zelinka, R. Senkerik, M. Pluhacek, "Do evolutionary algorithms indeed require randomness?" 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2283-2289, ISBN 978-1-4799-0453-2. [10] J. C. Sprott, Chaos and Time-Series Analysis, Oxford University Press, 2003. [11] M. Matsumoto, T. Nishimura: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS) 8(1), 3-30 (1998). [12] S. Wolfram, Mathematica: A System for Doing Mathematics by Computer, Second Edition. Redwood City: Addison-Wesley, 1991. [13] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. [14] J. Kennedy, “The particle swarm: social adaptation of knowledge,” in Proceedings of the IEEE International Conference on Evolutionary Computation, 1997, pp. 303–308. [15] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1998, pp. 69–73.I. S. [16] R. Cheng and Y. Jin, "A Competitive Swarm Optimizer for Large Scale Optimization," in IEEE Transactions on Cybernetics, vol. 45, no. 2, pp. 191-204, Feb. 2015. [17] M. Pluhacek, T. Kadavy, R. Senkerik, A. Viktorin and I. Zelinka, "Comparing selected PSO modifications on CEC 15 benchmark set," 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, 2016, pp. 1-6. [18] Y. Shi, R.C. Eberhart, Parameter selection in particle swarm optimization, in: Proceedings of the Seventh Annual Conference on Evolutionary Programming, New York, USA, 1998, pp. 591–600. [19] Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization, in: Proceedings of the IEEE Congress on Evolutionary Computation, IEEE Press, 1999, pp. 1945–1950. [20] F. van den Bergh, A.P. Engelbrecht, A study of particle swarm optimization particle trajectories, Information Sciences, Volume 176, Issue 8, 22 April 2006, pages 937-971, ISSN 0020-0255, [21] A. P. Engelbrecht, "Particle Swarm Optimization: Iteration Strategies Revisited," 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, Ipojuca, 2013, pp. 119-123. [22] M. Pluhacek, R. Senkerik, I. Zelinka and D. Davendra, Chaos Driven PSO–On the Influence of Various CPRNG Implementations–An Initial Study. In ISCS 2014: Interdisciplinary Symposium on Complex Systems, 2015, pp. 225-237. Springer International Publishing.
utb.fulltext.sponsorship This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014. Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2017/004.
utb.wos.affiliation [Pluhacek, Michal; Senkerik, Roman; Viktorin, Adam; Kadavy, Tomas] Tomas Bata Univ Zlin, Fac Appl Informat, Stranemi 4511, Zlin 76005, Czech Republic
utb.fulltext.projects P103/15/06700S
utb.fulltext.projects LO1303 (MSMT-7778/20140
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2017/004
Find Full text

Files in this item

Show simple item record