Analysis of ımproved evolutionary algorithms using students' datasets

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorAyaz, Muhammad
dc.contributor.authorNgo-Hoang, Dai-Long
dc.contributor.authorTabuena, Almighty C.
dc.contributor.authorRabbi, Fazle
dc.contributor.authorTilaye, Getahun Fikadu
dc.contributor.authorBassey, Mbiatke Anthony
dc.date.accessioned2023-01-20T08:01:18Z
dc.date.available2023-01-20T08:01:18Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractEvolutionary Algorithms (EAs) are powerful heuristic search approaches which relies on Darwinian evolution that capture global solutions to complex optimization problems which has powerful features of reliability and versatility. (EAs) such as Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, chaotic map and dynamic-weight Particle Swarm Optimization (CHDPSOA) are combined with PSO to enhance the search strategy through adjusting the inertia weight of PSO and changing the position update formula in the (CHDPSOA), resulting in efficient balancing for local and global PSO feature selection processes. The performance of CHDPSOA was compared to that of three metaheuristic techniques: Differential Evolution (DE) and the original PSO, using eight numerical functions. The validation of this technique is carried out on four different datasets. The results show that the CHDPSOA is a good feature selection technique that balances the exploration and exploitation search processes to produce good results. The proposed CHDPSOA method performed well in correctly categorizing features using the KNN Classifier for all four datasets.en_US
dc.identifier.doi10.1109/I2CACIS54679.2022.9815272en_US
dc.identifier.scopus2-s2.0-85134732119en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6101
dc.identifier.urihttps://doi.org/10.1109/I2CACIS54679.2022.9815272
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2022 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectEvolutionary Algorithms, Chaotic Map, Particle swarm optimization, Feature selection, Differential evolutionen_US
dc.titleAnalysis of ımproved evolutionary algorithms using students' datasetsen_US
dc.typeConference Objecten_US

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