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How unconventional chaotic pseudo-random generators influence population diversity in differential evolution

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dc.title How unconventional chaotic pseudo-random generators influence population diversity in differential evolution en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.issn 0302-9743 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783319912523
dc.date.issued 2018
utb.relation.volume 10841 LNAI
dc.citation.spage 524
dc.citation.epage 535
dc.event.title 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018
dc.event.location Zakopane
utb.event.state-en Poland
utb.event.state-cs Polsko
dc.event.sdate 2018-06-03
dc.event.edate 2018-06-07
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-91253-0_49
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-91253-0_49
dc.subject Chaotic map en
dc.subject Complex dynamics en
dc.subject Deterministic chaos en
dc.subject Differential evolution en
dc.subject Population diversity en
dc.description.abstract This research focuses on the modern hybridization of the discrete chaotic dynamics and the evolutionary computation. It is aimed at the influence of chaotic sequences on the population diversity as well as at the algorithm performance of the simple parameter adaptive Differential Evolution (DE) strategy: jDE. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the chaotic pseudo-random number generators. The population diversity and jDE convergence are recorded for 15 test functions from the CEC 2015 benchmark set in 30D. © Springer International Publishing AG, part of Springer Nature 2018. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008003
utb.identifier.obdid 43879136
utb.identifier.scopus 2-s2.0-85048036682
utb.source d-scopus
dc.date.accessioned 2018-07-27T08:47:39Z
dc.date.available 2018-07-27T08:47:39Z
dc.description.sponsorship 2018/177; IC406; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; 710577, Horizon 2020; CA15140; IGA/CebiaTech/2018/003; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.affiliation Roman Senkerik 1( ✉ ) http://orcid.org/0000-0002-5839-4263 , Adam Viktorin 1 http://orcid.org/0000-0003-0861-0340 , Michal Pluhacek 1 http://orcid.org/0000-0002-3692-2838 , Tomas Kadavy 1 http://orcid.org/0000-0002-3341-4336 , and Ivan Zelinka 2 http://orcid.org/0000-0002-3858-7340 1 Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic {senkerik,aviktorin,pluhacek,kadavy}@utb.cz 2 Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic ivan.zelinka@vsb.cz
utb.fulltext.dates -
utb.fulltext.references 1. Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans. Evol. Comput. 7(3), 289–304 (2003) 2. dos Santos Coelho, L., Mariani, V.C.: A novel chaotic particle swarm optimization approach using H ́enon map and implicit filtering local search for economic load dispatch. Chaos Solitons Fractals 39(2), 510–518 (2009) 3. Davendra, D., Zelinka, I., Senkerik, R.: Chaos driven evolutionary algorithms for the task of PID control. Comput. Math. Appl. 60(4), 1088–1104 (2010) 4. Pluhacek, M., Senkerik, R., Davendra, D., Oplatkova, Z.K., Zelinka, I.: On the behavior and performance of chaos driven PSO algorithm with inertia weight. Comput. Math. Appl. 66(2), 122–134 (2013) 5. Pluhacek, M., Senkerik, R., Davendra, D.: Chaos particle swarm optimization with eensemble of chaotic systems. Swarm Evol. Comput. 25, 29–35 (2015) 6. Metlicka, M., Davendra, D.: Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems. Swarm Evol. Comput. 25, 15–28 (2015) 7. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013) 8. Wang, G.G., Guo, L., Gandomi, A.H., Hao, G.S., Wang, H.: Chaotic Krill Herd algorithm. Inf. Sci. 274, 17–34 (2014) 9. Zhang, C., Cui, G., Peng, F.: A novel hybrid chaotic ant swarm algorithm for heat exchanger networks synthesis. Appl. Therm. Eng. 104, 707–719 (2016) 10. Jordehi, A.R.: Chaotic bat swarm optimisation (CBSO). Appl. Soft Comput. 26, 523–530 (2015) 11. Wang, G.G., Deb, S., Gandomi, A.H., Zhang, Z., Alavi, A.H.: Chaotic cuckoo search. Soft. Comput. 20(9), 3349–3362 (2016) 12. dos Santos Coelho, L., Ayala, H.V.H., Mariani, V.C.: A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization. Appl. Math. Comput. 234, 452–459 (2014) 13. Zamuda, A., Brest, J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm Evol. Comput. 25, 72–99 (2015) 14. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006) 15. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution-an updated survey. Swarm Evol. Comput. 27, 1–30 (2016) 16. Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665. IEEE (2014) 17. Senkerik, R., Pluhacek, M., Zelinka, I., Davendra, D., Janostik, J.: Preliminary study on the randomization and sequencing for the chaos embedded heuristic. In: Abraham, A., Wegrzyn-Wolska, K., Hassanien, A.E., Snasel, V., Alimi, A.M. (eds.) Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015. AISC, vol. 427, pp. 591–601. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29504-6 55 18. Senkerik, R., Pluhacek, M., Viktorin, A., Kadavy, T.: On the randomization of indices selection for differential evolution. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) CSOC 2017. AISC, vol. 573, pp. 537–547. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57261-1 53 19. Senkerik, R., Pluhacek, M., Zelinka, I., Viktorin, A., Kominkova Oplatkova, Z.: Hybridization of multi-chaotic dynamics and adaptive control parameter adjusting jDE strategy. In: Matoušek, R. (ed.) ICSC-MENDEL 2016. AISC, vol. 576, pp. 77–87. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58088-3 8 20. Sprott, J.C., Sprott, J.C.: Chaos and time-series analysis, vol. 69. Citeseer (2003) 21. Poláková, R., Tvrdík, J., Bujok, P., Matoušek, R.: Population-size adaptation through diversity-control mechanism for differential evolution. In: MENDEL, 22th International Conference on Soft Computing, pp. 49–56 (2016) 22. Viktorin, A., Pluhacek, M., Senkerik, R.: Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4797–4803. IEEE (2016)
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 (MSMT-7778/2014), further 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. IGA/CebiaTech/2018/003. This work is also based upon support by COST Action CA15140 (ImAppNIO), and COST Action IC406 (cHiPSet). Prof. Zelinka acknowledges following grants/projects: SGS No. 2018/177, VSB-TUO and by the EU’s Horizon 2020 research and innovation programme under grant agreement No. 710577.
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, Czech Republic; Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. listopadu 15, Poruba, Ostrava, Czech Republic
utb.fulltext.projects LO1303 (MSMT-7778/2014)
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects COST Action CA15140 (ImAppNIO)
utb.fulltext.projects COST Action IC406 (cHiPSet)
utb.fulltext.projects SGS 2018/177
utb.fulltext.projects H2020 710577
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