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Efficient time-delay system optimization with auto-configured metaheuristics

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dc.title Efficient time-delay system optimization with auto-configured metaheuristics en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Byrski, Aleksander
dc.contributor.author Guzowski, Hubert
dc.contributor.author Janků, Peter
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Matušů, Radek
dc.contributor.author Pluháček, Michal
dc.contributor.author Pekař, Libor
dc.contributor.author Smołka, Maciej
dc.contributor.author Viktorin, Adam
dc.relation.ispartof Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
dc.identifier.issn 1062-922X Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 979-835033702-0
dc.date.issued 2023
dc.citation.spage 1084
dc.citation.epage 1089
dc.event.title 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
dc.event.location hybrid, Honolulu
utb.event.state-en United States
utb.event.state-cs Spojené státy americké
dc.event.sdate 2023-10-01
dc.event.edate 2023-10-04
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/SMC53992.2023.10394087
dc.relation.uri https://ieeexplore.ieee.org/document/10394087
dc.relation.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10394087
dc.description.abstract This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. en
utb.faculty Faculty of Applied Informatics
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011944
utb.identifier.obdid 43885277
utb.identifier.scopus 2-s2.0-85187295872
utb.identifier.coden PICYE
utb.source d-scopus
dc.date.accessioned 2024-04-17T13:11:48Z
dc.date.available 2024-04-17T13:11:48Z
dc.description.sponsorship Faculty of Applied Informatics, Tomas Bata University in Zlin; Tomas Bata University in Zlín, TBU, (IGA/CebiaTech/2023/004); Grantová Agentura České Republiky, GA ČR, (GF21-45465L); Narodowe Centrum Nauki, NCN, (2020/39/I/ST7/02285); Ministerstwo Edukacji i Nauki, MNiSW
utb.ou Department of Informatics and Artificial Intelligence
utb.ou Department of Automation and Control Engineering
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Janků, Peter
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Matušů, Radek
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Pekař, Libor
utb.contributor.internalauthor Viktorin, Adam
utb.fulltext.affiliation Roman Senkerik1, Aleksander Byrski3, Hubert Guzowski3, Peter Janku1, Tomas Kadavy1, Zuzana Kominkova Oplatkova1, Radek Matusu2, Michal Pluhacek1, Libor Pekar2, Maciej Smolka3, Adam Viktorin1 1 Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic {senkerik,oplatkova}@utb.cz 2 Department of Automation and Control Engineering, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic {pekar,rmatusu}@utb.cz 3 Institute of Computer Science, AGH University of Science and Technology, Krakow, Poland {guzowski,smolka,olekb}@agh.edu.pl
utb.fulltext.sponsorship The research presented in this paper was partially supported by: NCN project no: 2020/39/I/ST7/02285, Polish Ministry of Education and Science funds assigned to AGH University of Science and Technology. It was also supported by Czech Science Foundation (GACR) project no: GF21-45465L, the Internal Grant Agency of the Tomas Bata University in Zlin, under project number IGA/CebiaTech/2023/004, and 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, A.I. Lab, Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence, Zlin, Czech Republic; Tomas Bata University, Faculty of Applied Informatics, Department of Automation and Control Engineering, Zlin, Czech Republic; Institute of Computer Science, Agh University of Science and Technology, Krakow, Poland
utb.fulltext.projects 2020/39/I/ST7/02285
utb.fulltext.projects GF21-45465L
utb.fulltext.projects IGA/CebiaTech/2023/004
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.ou Department of Informatics and Artificial Intelligence
utb.fulltext.ou Department of Automation and Control Engineering
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