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Hypersphere universe boundary method comparison on HCLPSO and PSO

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dc.title Hypersphere universe boundary method comparison on HCLPSO and PSO en
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Pluháček, Michal
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
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 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-59650-1
dc.identifier.isbn 978-3-319-59649-5
dc.date.issued 2017
utb.relation.volume 10334 LNCS
dc.citation.spage 173
dc.citation.epage 182
dc.event.title 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017
dc.event.location Logroño (La Rioja)
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2017-06-21
dc.event.edate 2017-06-23
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-59650-1_15
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-59650-1_15
dc.subject Comprehensive Learning en
dc.subject HCLPSO en
dc.subject Heterogeneous en
dc.subject Particle Swarm Optimization en
dc.subject PSO en
dc.subject Roaming particles en
dc.subject Search space boundaries en
dc.description.abstract In this paper, the hypersphere universe method is applied on Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO) and a classical representative of swarm intelligence Particle Swarm Optimization (PSO). The goal is to the compare this method to the classical version of these algorithms. The comparisons are made on CEC’17 benchmark set functions. The experiments were carried out according to CEC benchmark rules and statistically evaluated using Friedman rank test. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007262
utb.identifier.obdid 43877163
utb.identifier.scopus 2-s2.0-85021746586
utb.identifier.wok 000432880600015
utb.source d-scopus
dc.date.accessioned 2017-09-03T21:40:07Z
dc.date.available 2017-09-03T21:40:07Z
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 Kadavý, Tomáš
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Tomas Kadavy ✉ , Michal Pluhacek, Adam Viktorin, and Roman Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {kadavy,pluhacek,aviktorin,senkerik}@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references 1. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995) 2. Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015) 3. Lynn, N.: Heterogeneous particle swarm optimization with an application of unit commitment in power system, Singapore. Thesis. School of Electrical and Electronic Engineering. Supervisor Ponnuthurai Nagaratnam Suganthan (2016) 4. Awad, N.H., et al.: Problem Definitions and Evaluation Criteria for CEC 2017 Special Session and Competition on Single-Objective Real-Parameter Numerical Optimization (2016) 5. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997) 6. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006) 7. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, pp. 84–88. IEEE (2000) 8. Friedman, M.: The use of ranks to avoid the assumption of normality implicits in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937) 9. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
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.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, Zlin, Czech Republic
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