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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 |