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Why tuning the control parameters of metaheuristic algorithms is so important for fair comparison?

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dc.title Why tuning the control parameters of metaheuristic algorithms is so important for fair comparison? en
dc.contributor.author Kazíková, Anežka
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Mendel
dc.identifier.issn 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2020
utb.relation.volume 26
utb.relation.issue 2
dc.citation.spage 9
dc.citation.epage 16
dc.type article
dc.language.iso en
dc.publisher Brno University of Technology
dc.identifier.doi 10.13164/mendel.2020.2.009
dc.relation.uri https://mendel-journal.org/index.php/mendel/article/view/120
dc.subject comparison en
dc.subject configuration en
dc.subject metaheuristics en
dc.subject parameter tuning en
dc.subject particle swarm optimization en
dc.subject swarm algorithms en
dc.description.abstract Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals. It is essential to pay attention to the quality of conducted experiments, especially when comparing several algorithms among themselves. The comparisons should be fair and unbiased. This paper points to the importance of proper initial parameter configurations of the compared algorithms. We highlight the performance differences with several popular and recommended parameter configurations. Even though the parameter selection was mostly based on comprehensive tuning experiments, the algorithms’ performance was surprisingly inconsistent, given various parameter settings. Based on the presented evidence, we conclude that paying attention to the metaheuristic algorithm’s parameter tuning should be an integral part of the development and testing processes. © 2020, Brno University of Technology. All rights reserved. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010151
utb.identifier.obdid 43881791
utb.identifier.scopus 2-s2.0-85098257247
utb.source j-scopus
dc.date.accessioned 2021-01-08T14:02:35Z
dc.date.available 2021-01-08T14:02:35Z
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Kazíková, Anežka
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Anezka Kazikova, Michal Pluhacek, Roman Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic kazikova@utb.cz, pluhacek@utb.cz, senkerik@utb.cz
utb.fulltext.dates Received: 25 October 2020 Accepted: 17 November 2020 Published: 21 December 2020
utb.fulltext.sponsorship This work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2020/001. 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.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2020/001
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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Attribution-NonCommercial-ShareAlike 4.0 International Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Attribution-NonCommercial-ShareAlike 4.0 International