Publikace UTB
Repozitář publikační činnosti UTB
Exploring clustering in SOMA
Repozitář DSpace/Manakin
Kontaktujte nás | Jazyk: čeština English
- Domovská stránka DSpace
- →
- Článek ve sborníku
- →
- Fakulta aplikované informatiky
- →
- Zobrazit záznam
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.title | Exploring clustering in SOMA | en |
| dc.contributor.author | Kadavý, Tomáš | |
| dc.contributor.author | Pluháček, Michal | |
| dc.contributor.author | Viktorin, Adam | |
| dc.contributor.author | Kazíková, Anežka | |
| dc.contributor.author | Šenkeřík, Roman | |
| dc.relation.ispartof | 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022 | |
| dc.identifier.isbn | 978-1-7281-7124-1 | |
| dc.date.issued | 2022 | |
| dc.event.title | 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022 | |
| dc.event.location | Florence | |
| utb.event.state-en | Italy | |
| utb.event.state-cs | Itálie | |
| dc.event.sdate | 2022-07-18 | |
| dc.event.edate | 2022-07-20 | |
| dc.type | conferenceObject | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.identifier.doi | 10.1109/COMPENG50184.2022.9905440 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/9905440 | |
| dc.relation.uri | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9905440 | |
| dc.description.abstract | During the developing phase of the new evolutionary algorithm (EA) or the analysis, several techniques or measurements are used to capture the inner dynamic of an algorithm. Besides the usual ones, for example, convergence graphs, population diversity, or complex networks, the scientists may also use clustering. Clustering analysis may naturally be used to analyze the grouping of individuals in swarm-based algorithms. This paper examines the possibilities of the clustering analysis for the Self-Organizing Migrating Algorithm with CLustering-aided migration (SOMA-CL). The algorithm is described in detail, together with several cluster analyses which can be used to investigate the behavior of the algorithm. © 2022 IEEE. | en |
| utb.faculty | Faculty of Applied Informatics | |
| dc.identifier.uri | http://hdl.handle.net/10563/1011258 | |
| utb.identifier.obdid | 43883725 | |
| utb.identifier.scopus | 2-s2.0-85141064129 | |
| utb.source | d-scopus | |
| dc.date.accessioned | 2023-01-06T08:03:59Z | |
| dc.date.available | 2023-01-06T08:03:59Z | |
| dc.description.sponsorship | IGA/CebiaTech/2022/001 | |
| utb.contributor.internalauthor | Kadavý, Tomáš | |
| utb.contributor.internalauthor | Pluháček, Michal | |
| utb.contributor.internalauthor | Viktorin, Adam | |
| utb.contributor.internalauthor | Kazíková, Anežka | |
| utb.contributor.internalauthor | Šenkeřík, Roman | |
| utb.fulltext.affiliation | Tomas Kadavy, Michal Pluhacek, Adam Viktorin, Anezka Kazikova, Roman Senkerik Faculty of Applied Informatics Tomas Bata University in Zlin T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {kadavy, pluhacek, aviktorin, kazikova, senkerik}@utb.cz | |
| utb.fulltext.dates | - | |
| utb.fulltext.references | [1] T. Bartz-Beielstein, C. Doerr, D. v. d. Berg, J. Bossek, S. Chandrasekaran, T. Eftimov, A. Fischbach, P. Kerschke, W. La Cava, M. Lopez-Ibanez et al., “Benchmarking in optimization: Best practice and open issues,” arXiv preprint arXiv:2007.03488, 2020. [2] I. Zelinka, “Soma—self-organizing migrating algorithm,” in SelfOrganizing Migrating Algorithm. Springer, 2016, pp. 3–49. [3] K. Price, N. Awad, M. Ali, and P. Suganthan, “Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization,” in Technical Report. Nanyang Technological University, 2018. [4] Q. B. Diep, I. Zelinka, S. Das, and R. Senkerik, “Soma t3a for solving the 100-digit challenge,” in Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing, A. Zamuda, S. Das, P. N. Suganthan, and B. K. Panigrahi, Eds. Cham: Springer International Publishing, 2020, pp. 155–165. [5] T. Kadavy, M. Pluhacek, A. Viktorin, and R. Senkerik, “Self-organizing migrating algorithm with clustering-aided migration,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, ser. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1441–1447. [Online]. Available: https://doi.org/10.1145/3377929.3398129 [6] A. Wagdy, A. A. Hadi, A. K. Mohamed, P. Agrawal, A. Kumar, and P. N. Suganthan, “Problem definitions and evaluation criteria for the cec 2021 special session and competition on single objective bound constrained numerical optimization.” Technical Report, Nanyang Technological University, Singapore, 2020. [7] I. Zelinka, “Soma—self-organizing migrating algorithm,” in New optimization techniques in engineering. Springer, 2004, pp. 167–217. [8] M. G. Omran, A. P. Engelbrecht, and A. Salman, “An overview of clustering methods,” Intelligent Data Analysis, vol. 11, no. 6, pp. 583–605, 2007. [9] J. A. Hartigan, Clustering algorithms. John Wiley & Sons, Inc., 1975. [10] I. Zelinka, “Soma—self-organizing migrating algorithm,” in Self-Organizing Migrating Algorithm. Springer, 2016, pp. 3–49. [11] S. Lloyd, “Least squares quantization in pcm,” IEEE transactions on information theory, vol. 28, no. 2, pp. 129–137, 1982. [12] G. Zames, N. Ajlouni, N. Ajlouni, N. Ajlouni, J. Holland, W. Hills, and D. Goldberg, “Genetic algorithms in search, optimization and machine learning.” Information Technology Journal, vol. 3, no. 1, pp. 301–302, 1981. [13] T. Kadavy, M. Pluhacek, R. Senkerik, and A. Viktorin, “Boundary strategies for self-organizing migrating algorithm analyzed using cec’17 benchmark,” in Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. Springer, 2019, pp. 58–69. [14] G. Ochoa and K. Malan, “Recent advances in fitness landscape analysis,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019, pp. 1077–1094. [15] P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of computational and applied mathematics, vol. 20, pp. 53–65, 1987. [16] M. Pluhacek, A. Viktorin, T. Kadavy, and A. Kazikova, “On the common population diversity measures in metaheuristics and their limitations,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021, pp. 1–7. | |
| utb.fulltext.sponsorship | This work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2022/001, and further by the resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz). | |
| utb.scopus.affiliation | Tomas Bata University in Zlin, Faculty of Applied Informatics, T.G. Masaryka 5555, Zlin, 760 01, Czech Republic | |
| utb.fulltext.projects | IGA/CebiaTech/2022/001 | |
| utb.fulltext.faculty | Faculty of Applied Informatics | |
| utb.fulltext.faculty | Faculty of Applied Informatics | |
| utb.fulltext.faculty | Faculty of Applied Informatics | |
| utb.fulltext.faculty | Faculty of Applied Informatics | |
| utb.fulltext.faculty | Faculty of Applied Informatics | |
| utb.fulltext.ou | - | |
| utb.fulltext.ou | - | |
| utb.fulltext.ou | - | |
| utb.fulltext.ou | - | |
| utb.fulltext.ou | - |
