Detection of object boundary from point cloud by using multi-population based differential evolution algorithm

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dc.authoridKARKINLI, AHMET EMIN/0000-0001-7216-6251
dc.contributor.authorKarkinli, Ahmet Emin
dc.date.accessioned2024-11-07T13:25:04Z
dc.date.available2024-11-07T13:25:04Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe problem-solving success of an Evolutionary Computing algorithm is too sensitive to the structures of mutation and crossover operators it uses. The mutation operator generates the trial vectors necessary for the relevant Evolutionary Computing method to perform efficient global and local search in the search space. Partial elitist mutation operators can produce more efficient trial vectors than isotropic mutation strategies. Another numerical genetic operator that affects the process of producing efficient trial vectors is crossover. Due to the dominant effect of the Evolutionary Computing algorithms of mutation and crossover operators on problem solving capacity, new numerical-genetic operators are constantly being developed. When solving a problem with the Differential Evolution Algorithm determining the ideal mutation operator and setting the initial values of the internal parameters of the crossover operator is quite time-consuming and difficult. In this paper, the Multi-population Based Differential Evolution Algorithm (MDE) has been proposed to solve real-valued numerical optimization problems with its convergence proof. The mutation operator of MDE is partial-elitist and its crossover operator is parameter-free, in practice. In this paper, 28 benchmark problems of CEC2013 with Dim = 20 and one real-world geometric optimization problem have been used in the experiments performed to examine the numerical problem-solving success of MDE. MDE's success in solving related benchmark problems has been statistically compared with ABC, CK, SOS and GWO. Statistical analysis of the results obtained from the experiments exposed that MDE is statistically more successful than comparison methods in solving numerical optimization problems used.
dc.identifier.doi10.1007/s00521-022-07969-w
dc.identifier.endpage5206
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85140992721
dc.identifier.scopusqualityQ1
dc.identifier.startpage5193
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07969-w
dc.identifier.urihttps://hdl.handle.net/11480/14464
dc.identifier.volume35
dc.identifier.wosWOS:000877881800005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectArtificial bee colony algorithm (ABC)
dc.subjectCuckoo search algorithm (CK)
dc.subjectSymbiotic organisms search (SOS)
dc.subjectGrey wolf optimization algorithm (GWO)
dc.subjectPoint cloud segmentation
dc.titleDetection of object boundary from point cloud by using multi-population based differential evolution algorithm
dc.typeArticle

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