Benchmarking global optimization techniques for unmanned aerial vehicle path planning

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dc.contributor.authorShehadeh, Mhd Alics
dc.contributor.authorKůdela, Jakubcs
dc.coverage.issueDeccs
dc.coverage.volume293cs
dc.date.accessioned2025-08-21T12:57:00Z
dc.date.available2025-08-21T12:57:00Z
dc.date.issued2025-12-01cs
dc.description.abstractThe Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select fourteen well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated. The code and results are available at a Zenodo repository https://doi.org/10.5281/zenodo.15424080.en
dc.formattextcs
dc.format.extent1-19cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationEXPERT SYSTEMS WITH APPLICATIONS. 2025, vol. 293, issue Dec, p. 1-19.en
dc.identifier.doi10.1016/j.eswa.2025.128645cs
dc.identifier.issn0957-4174cs
dc.identifier.orcid0000-0001-5550-4650cs
dc.identifier.orcid0000-0002-4372-2105cs
dc.identifier.other198283cs
dc.identifier.researcheridCAG-1272-2022cs
dc.identifier.researcheridP-7327-2018cs
dc.identifier.scopus56769626500cs
dc.identifier.urihttps://hdl.handle.net/11012/255465
dc.language.isoencs
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDcs
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONScs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S095741742502264Xcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0957-4174/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectUnmanned aerial vehicleen
dc.subjectPath planningen
dc.subjectBenchmarkingen
dc.subjectGlobal optimizationen
dc.subjectExploratory landscape analysisen
dc.subjectVariable dimension problemen
dc.titleBenchmarking global optimization techniques for unmanned aerial vehicle path planningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/MSM/EH/EH22_008/0004634cs
sync.item.dbidVAV-198283en
sync.item.dbtypeVAVen
sync.item.insts2025.08.21 14:56:59en
sync.item.modts2025.08.21 14:34:16en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inÅŸenÃœrství. Ústav automatizace a informatikycs
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