Analyzing the performances of evolutionary multi-objective optimizers on design optimization of robot gripper configurations

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dc.authoridATILA, UMIT/0000-0002-1576-9977
dc.authoridDorterler, Murat/0000-0003-1127-515X
dc.authoridDurgut, Rafet/0000-0002-6891-5851
dc.authoridsahin, ismail/0000-0001-8566-3433
dc.contributor.authorDorterler, Murat
dc.contributor.authorAtila, Umit
dc.contributor.authorDurgut, Rafet
dc.contributor.authorSahin, Ismail
dc.date.accessioned2024-09-29T16:08:20Z
dc.date.available2024-09-29T16:08:20Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractRobot grippers are widely used in a variety of areas requiring automation, precision, and safety. The performance of the grippers is directly associated with their design. In this study, four different multiobjective metaheuristic algorithms including particle swarm optimization (MOPSO), artificial algae algorithm (MOAAA), grey wolf optimizer (MOGWO) and nondominated sorting genetic algorithm (NSGA-II) were applied to two different configurations of highly nonlinear and multimodal robot gripper design problem including two objective functions and a certain number of constraints. The first objective is to minimize the difference between minimum and maximum forces for the assumed range in which the gripper ends are displaced. The second objective is force transmission rate that is the ratio of the actuator force to the minimum holding force obtained at the gripper ends. The performance of the optimizers was examined separately for each configuration by using pareto-front curves and hyper-volume (HV) metric. Performances of the optimizers on the specific problem were compared with results of previously proposed algorithms under equal conditions. With respect to these comparisons, the best-known results of the configurations were obtained. Furthermore, the pareto optimal solutions are thoroughly examined to present the relationship between design variables and objective functions.en_US
dc.identifier.doi10.3906/elk-2003-140
dc.identifier.endpage369en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85100955839en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage349en_US
dc.identifier.trdizinid514179en_US
dc.identifier.urihttps://doi.org/10.3906/elk-2003-140
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/514179
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7497
dc.identifier.volume29en_US
dc.identifier.wosWOS:000614434700003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRobot grippersen_US
dc.subjectengineering optimizationen_US
dc.subjectmultiobjective optimizationen_US
dc.subjectdesign optimizationen_US
dc.subjectmetaheuristicen_US
dc.titleAnalyzing the performances of evolutionary multi-objective optimizers on design optimization of robot gripper configurationsen_US
dc.typeArticleen_US

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