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Multi-swarm optimization algorithm based on firefly and particle swarm optimization techniques

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dc.title Multi-swarm optimization algorithm based on firefly and particle swarm optimization techniques 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 978-3-319-91252-3
dc.date.issued 2018
utb.relation.volume 10841 LNAI
dc.citation.spage 405
dc.citation.epage 416
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_38
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-91253-0_38
dc.subject Firefly Algorithm en
dc.subject Particle Swarm Optimization en
dc.subject Hybridization en
dc.subject Multi-swarm en
dc.description.abstract In this paper, the two hybrid swarm-based metaheuristic algorithms are tested and compared. The first hybrid is already existing Firefly Particle Swarm Optimization (FFPSO), which is based, as the name suggests, on Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). The secondly proposed hybrid is an algorithm using the multi-swarm method to merge FA and PSO. The performance of our developed algorithm is tested and compared with the FFPSO and canonical FA. Comparisons have been conducted on five selected benchmark functions, and the results have been evaluated for statistical significance using Friedman rank test. © Springer International Publishing AG, part of Springer Nature 2018. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008001
utb.identifier.obdid 43878940
utb.identifier.scopus 2-s2.0-85048045751
utb.identifier.wok 000552718500038
utb.source d-scopus
dc.date.accessioned 2018-07-27T08:47:39Z
dc.date.available 2018-07-27T08:47:39Z
dc.description.sponsorship IC1406, COST, European Cooperation in Science and Technology; CA15140, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; COST, European Cooperation in Science and Technology; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund
dc.description.sponsorship 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/2018/003]; COST (European Cooperation in Science Technology) [CA15140, IC1406]
utb.ou CEBIA-Tech
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 ( ✉ ) http://orcid.org/0000-0002-3341-4336 , Michal Pluhacek http://orcid.org/0000-0002-3692-2838 , Adam Viktorin http://orcid.org/0000-0003-0861-0340 , and Roman Senkerik http://orcid.org/0000-0002-5839-4263 Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic {kadavy,pluhacek,aviktorin,senkerik}@utb.cz
utb.fulltext.dates -
utb.fulltext.references 1. Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T., Zelinka, I.: A review of real-world applications of particle swarm optimization algorithm. In: Duy, V., Dao, T., Zelinka, I., Kim, S., Phuong, T. (eds.) AETA 2017. LNEE. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-69814-4 112018 2. Du, W., Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inf. Sci. 178(15), 3096–3109 (2008) 3. Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 20(279), 587–603 (2014) 4. Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24653-4 50 5. Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer with local search. IEEE (2005) 6. Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 1(24), 11–24 (2015) 7. Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO for real-parameter optimization. IEEE (2013) 8. Zhan, Z.-H., Zhang, J., Li, Y., Shi, Y.-H.: Orthogonal learning particle swarm optimization. TEVC 15(6), 832–847 (2011) 9. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995) 10. Allahverdi, A., Al-Anzi, F.S.: A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput. Oper. Res. 33(4), 1056–1080 (2006) 11. Assareh, E., Behrang, M.A., Assari, M.R., Ghanbarzadeh, A.: Application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques on demand estimation of oil in Iran. Energy 35(12), 5223–5229 (2010) 12. Rudek, M., Canciglieri Jr., O., Greboge, T.: A PSO application in skull prosthesis modelling by superellipse. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 12(2), 1–12 (2013). https://doi.org/10.5565/rev/elcvia.514 13. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome (2010) 14. 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) 15. Yang, X.: Firefly algorithm, Lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI, pp. 209–218. Springer, London (2010). https://doi.org/10.1007/978-1-84882-983-1 15 16. Farahani, S.M., Abshouri, A.A., Nasiri, B., Meybodi, M.R.: A Gaussian firefly algorithm. Int. J. Mach. Learn. Comput. 1(5), 448 (2011) 17. Kora, P., Rama Krishna, K.S.: Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block. Int. J. Cardiovasc. Acad. 2(1), 44–48 (2016)
utb.fulltext.sponsorship T. Kadavy—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 under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.wos.affiliation [Kadavy, Tomas; Pluhacek, Michal; Viktorin, Adam; Senkerik, Roman] Tomas Bata Univ Zlin, Fac Appl Informat, TG Masaryka 5555, Zlin 76001, Czech Republic
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 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 CA15140
utb.fulltext.projects ImAppNIO
utb.fulltext.projects cHiPSet
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