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Inner dynamics of particle swarm optimization interpreted by complex network analysis
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| dc.title | Inner dynamics of particle swarm optimization interpreted by complex network analysis | en |
| dc.contributor.author | Pluháček, Michal | |
| dc.contributor.author | Kadavý, Tomáš | |
| dc.contributor.author | Kazíková, Anežka | |
| dc.contributor.author | Viktorin, Adam | |
| 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/COMPENG50184.2022.9905435 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/9905435 | |
| dc.relation.uri | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9905435 | |
| dc.subject | particle swarm optimization | en |
| dc.subject | complex network | en |
| dc.subject | population diversity | en |
| dc.description.abstract | In this paper, we present the relation between the inner dynamics of the particle swarm optimization algorithm and the properties of a complex network recording the information transfer in the population. Using population diversity as an example, we argue that the complex network analysis is a viable tool for assessing the state of the population and the eventual necessity of an adaptive intervention. © 2022 IEEE. | en |
| utb.faculty | Faculty of Applied Informatics | |
| dc.identifier.uri | http://hdl.handle.net/10563/1011257 | |
| utb.identifier.obdid | 43884094 | |
| utb.identifier.scopus | 2-s2.0-85141085054 | |
| 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 | Pluháček, Michal | |
| utb.contributor.internalauthor | Kadavý, Tomáš | |
| utb.contributor.internalauthor | Kazíková, Anežka | |
| utb.contributor.internalauthor | Viktorin, Adam | |
| utb.contributor.internalauthor | Šenkeřík, Roman | |
| utb.fulltext.affiliation | Michal Pluhacek, Tomas Kadavy, Anezka Kazikova, Adam Viktorin and Roman Senkerik Faculty of Applied Informatics Tomas Bata University in Zlin T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {pluhacek, kadavy, kazikova, aviktorin, senkerik}@utb.cz | |
| utb.fulltext.dates | - | |
| utb.fulltext.references | [1] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. [2] Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in Proceedings of the IEEE International Conference on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1998, pp. 69–73.I. S. [3] J. Kennedy, "The particle swarm: social adaptation of knowledge," in Proceedings of the IEEE International Conference on Evolutionary Computation, 1997, pp. 303–308. [4] F. van den Bergh, A.P. Engelbrecht, A study of particle swarm optimization particle trajectories, Information Sciences, Volume 176, Issue 8, 22 April 2006, pages 937-971, ISSN 0020-0255, [5] M. Montes de Oca, J. Pena, T. Stutzle, C. Pinciroli, and M. Dorigo. "Heterogeneous particle swarm optimizers," in Proceedings of the IEEE Congress on Evolutionary Computation, 2009, pp. 698–705. [6] A. Ratnaweera, S. Halgamuge, and H. Watson, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004. [7] Deng, W., Zhao, H., Yang, X., Xiong, J., Sun, M., & Li, B. (2017). Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Applied Soft Computing, 59, 288-302. [8] Morrison, R. W., & Jong, K. A. D. (2001, October). Measurement of population diversity. In International conference on artificial evolution (evolution artificielle) (pp. 31-41). Springer, Berlin, Heidelberg. [9] Pluhacek, M., Viktorin, A., Kadavy, T., & Kazikova, A. (2021, December). On the common population diversity measures in metaheuristics and their limitations. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE. [10] I. Zelinka, D. Davendra, R. Senkerik, R. Jasek,: Do Evolutionary Algorithm Dynamics Create Complex Network Structures? Complex Systems 2, 0891–2513, 20, 127–140, 2011 [11] I. Zelinka, D. Davendra, M. Chadli, R. Senkerik, T.T. Dao, L. Skanderova,:Evolutionary Dynamics as The Structure of Complex Networks. In: Zelinka, I.,Snasel, V., Abraham, A. (eds.) Handbook of Optimization. ISRL, vol. 38, pp. 215–243. Springer, Heidelberg (2013) [12] I. Zelinka, Investigation on relationship between complex network and evolutionary algorithms dynamics, AIP Conference Proceedings 1389 (1) 1011–1014, 2011. [13] D. Davendra, I. Zelinka, R. Senkerik. and M. Pluhacek, Complex Network Analysis of Discrete Self-organising Migrating Algorithm, in: Zelinka, I. and Suganthan, P. and Chen, G. and Snasel, V. and Abraham, A. and Rossler, O. (Eds.) Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, Advances in Intelligent Systems and Computing, Springer Berlin Heidelberg, pp. 161–174 , 2014. [14] D. Davendra, I. Zelinka, M. Metlicka, R. Senkerik, M. Pluhacek, "Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem," Differential Evolution (SDE), 2014 IEEE Symposium on , vol., no., pp.1,8, 9-12 Dec, 2014 [15] Pluhacek, M., Janostik, J., Senkerik, R., Zelinka, I., & Davendra, D. (2016). PSO as complex network—capturing the inner dynamics—initial study. In Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015 (pp. 551-559). Springer, Cham. [16] Zhang, Junlong, and Yu Luo. "Degree centrality, betweenness centrality, and closeness centrality in social network." 2017 2nd international conference on modelling, simulation and applied mathematics (MSAM2017). Atlantis Press, 2017. [17] R. Poláková, J. Tvrdík, P. Bujok, R. Matoušek, Population-size adaptation through diversity-control mechanism for differential evolution. In MENDEL, 22th International Conference on Soft Computing, 2016, (pp. 49-56). [18] Wolfram Language. (2012). GraphLinkEfficiency. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/GraphLinkEfficiency.html | |
| 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, 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 | |
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