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PSO with attractive search space border points

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dc.title PSO with attractive search space border points en
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Kadavý, Tomáš
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 9783319590592
dc.date.issued 2017
utb.relation.volume 10246 LNAI
dc.citation.spage 665
dc.citation.epage 675
dc.event.title 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
dc.event.location Zakopane
utb.event.state-en Poland
utb.event.state-cs Polsko
dc.event.sdate 2017-06-11
dc.event.edate 2017-06-15
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-59060-8_60
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-59060-8_60
dc.subject Diversity en
dc.subject Particle swarm optimization en
dc.subject PSO en
dc.description.abstract One of the biggest drawbacks of the original Particle Swarm Optimization is the premature convergence and fast loss of diversity in the population. In this paper, we propose and discuss a simple yet effective modification to help the PSO maintain diversity and avoid premature convergence. The particles are randomly attracted towards the border points of the search space. We use the CEC13 Benchmark function set to test the performance of proposed method and compare it to original PSO. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007184
utb.identifier.obdid 43877146
utb.identifier.scopus 2-s2.0-85020887619
utb.identifier.wok 000426206100060
utb.source d-scopus
dc.date.accessioned 2017-09-03T21:39:57Z
dc.date.available 2017-09-03T21:39:57Z
dc.description.sponsorship Grant Agency of the Czech Repub-lic - GACR [P103/15/06700S]; Ministry of Education, Youth and Sports of the Czech Republic within National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under 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 ( B ) , Roman Senkerik, Adam Viktorin, and Tomas Kadavy Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {pluhacek,senkerik,aviktorin,kadavy}@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. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 69–73. I. S (1998) 3. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997) 4. Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011). ISSN 1568-4946 5. Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Technical report 2 (2002) 6. Liang, J.J., Qu, B.-Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization. Technical report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore (2013) 7. Van Den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Inf. Sci. 176(8), 937–971 (2006) 8. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA, pp. 84–88 (2000)
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 Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, Czech Republic
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