New running technique for the bison algorithm

Kazíková, Anežka; Pluháček, Michal; Viktorin, Adam; Šenkeřík, Roman

dc.title	New running technique for the bison algorithm	en
dc.contributor.author	Kazíková, Anežka
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	417
dc.citation.epage	426
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_39
dc.relation.uri	https://link.springer.com/chapter/10.1007/978-3-319-91253-0_39
dc.subject	Bison algorithm	en
dc.subject	Running technique	en
dc.subject	Swarm algorithms	en
dc.description.abstract	This paper examines the performance of the Bison Algorithm with a new running technique. The Bison Algorithm was inspired by the typical behavior of bison herds: the swarming movement of endangered bison as the exploitation factor and the running as the exploration phase of the optimization. While the original running procedure allowed the running groupto scatter throughout the search space, the new approach proposed in this paper preserves the initial formation of the running group throughout the optimization process. At the beginning of the paper, we introduce the Bison Algorithm and explain the new running technique procedure. Later the performance of the adjusted algorithm is tested and compared to the Particle Swarm Optimization and the Cuckoo Search algorithm on the IEEE CEC 2017 benchmark set, consisting of 30 functions. Finally, we evaluate the meaning of the experiment outcomes for future research. © Springer International Publishing AG, part of Springer Nature 2018.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1008002
utb.identifier.obdid	43878973
utb.identifier.scopus	2-s2.0-85048062416
utb.identifier.wok	000552718500039
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; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund; COST, European Cooperation in Science and Technology
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 and Technology)European Cooperation in Science and Technology (COST) [CA15140, IC1406]
utb.ou	CEBIA-Tech
utb.contributor.internalauthor	Kazíková, Anežka
utb.contributor.internalauthor	Pluháček, Michal
utb.contributor.internalauthor	Viktorin, Adam
utb.contributor.internalauthor	Šenkeřík, Roman
utb.fulltext.affiliation	Anezka Kazikova ( ✉ ) , 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 {kazikova,pluhacek,aviktorin,senkerik}@utb.cz
utb.fulltext.dates	-
utb.fulltext.references	1. Ouaarab, A., Ahiod, B., Yang, X.S.: Discrete Cuckoo Search algorithm for the travelling salesman problem. Neural Comput. Appl. 24(7–8), 1659–1669 (2014) 2. Duan, Y., Ying, S.: A particle swarm optimization algorithm with ant search for solving traveling salesman problem. In: International Conference on Computational Intelligence and Security, Beijing, pp. 137–141 (2009) 3. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995) 4. Yang, X.-S.: 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 5. Yang, X.-S., Deb, S.: Cuckoo Search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214, India, December 2009. IEEE Publications (2009) 6. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014) 7. Kazikova, A., Pluhacek, M., Senkerik R., Viktorin, A.: Proposal of a new swarm optimization method inspired in Bison behavior. In: Matousek, R. (ed.) Recent Advances in Soft Computing (Mendel 2017). Advances in Intelligent Systems and Computing. Springer, Heidelberg (2017, in press) 8. Faris, H., Aljarah, I., Mirjalili, S., Castillo, P., Merelo, J.: EvoloPy: an open-source nature-inspired optimization framework in Python. In: Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016), ECTA, vol. 1, pp. 171–177 (2016) 9. Awad, N.H., Ali, M.Z., Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Technical report, Nanyang Technological University, Singapore (2016) 10. Berman, R.: American Bison (Nature Watch). Lerner Publications, Minneapolis (2008)
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 under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science and 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	[Kazikova, Anezka; Pluhacek, Michal; Viktorin, Adam; Senkerik, Roman] Tomas Bata Univ Zlin, Fac Appl, 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	IC1406
utb.fulltext.projects	cHiPSet