Nature-Inspired Algorithms in Real-World Optimization Problems

dc.contributor.authorBujok, Petr
dc.contributor.authorTvrdik, Josef
dc.contributor.authorPolakova, Radka
dc.coverage.issue1cs
dc.coverage.volume23cs
dc.date.accessioned2019-06-26T10:18:07Z
dc.date.available2019-06-26T10:18:07Z
dc.date.issued2017-06-01cs
dc.description.abstractEight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.en
dc.formattextcs
dc.format.extent7-14cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2017 vol. 23, č. 1, s. 7-14. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2017.1.007en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/179192
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/42cs
dc.rights.accessopenAccessen
dc.subjectGlobal optimizationen
dc.subjectreal-world optimization problemsen
dc.subjectnature-inspired algorithmsen
dc.subjectadaptive differential evolutionen
dc.subjectexperimental comparisonen
dc.titleNature-Inspired Algorithms in Real-World Optimization Problemsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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