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dc.contributor.authorKalra, Minakshi
dc.contributor.authorKumar, Vijay
dc.contributor.authorKaur, Manjit
dc.contributor.authorIdris, Sahar Ahmed
dc.contributor.authorOzturk, Saban
dc.contributor.authorAlshazly, Hammam
dc.date.accessioned2024-03-12T19:34:34Z
dc.date.available2024-03-12T19:34:34Z
dc.date.issued2022
dc.identifier.issn1546-2218
dc.identifier.issn1546-2226
dc.identifier.urihttps://doi.org/10.32604/cmc.2022.020682
dc.identifier.urihttps://hdl.handle.net/20.500.12450/2636
dc.description.abstractNowadays, due to the increase in information resources, the number of parameters and complexity of feature vectors increases. Optimization methods offer more practical solutions instead of exact solutions for the solution of this problem. The Emperor Penguin Optimizer (EPO) is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins. It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features. Although traditional EPO overcomes the optimization problems in continuous search space, many problems today shift to the binary search space. Therefore, in this study, using the power of traditional EPO, binary EPO (BEPO) is presented for the effective solution of binary-nature problems. BEPO algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search space. For this purpose, the sigmoidal functions are preferred in determining the emperor positions. In addition, the boundaries of the search space remain constant by choosing binary operators. BEPO's performance is evaluated over twenty-nine benchmarking functions. Statistical evaluations are made to reveal the superiority of the BEPO algorithm. In addition, the performance of the BEPO algorithm was evaluated for the binary feature selection problem. The experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.en_US
dc.description.sponsorshipDeanship of Scientific Research at King Khalid University [RGP.1/95/42]en_US
dc.description.sponsorshipThis work was supported by the Deanship of Scientific Research at King Khalid University through the Research Groups Program under Grant Number RGP.1/95/42.en_US
dc.language.isoengen_US
dc.publisherTech Science Pressen_US
dc.relation.ispartofCmc-Computers Materials & Continuaen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMetaheuristicsen_US
dc.subjectoptimization algorithmsen_US
dc.subjectemperor penguin optimizeren_US
dc.subjectintensificationen_US
dc.subjectdiversificationen_US
dc.subjectfeature selectionen_US
dc.titleA Novel Binary Emperor Penguin Optimizer for Feature Selection Tasksen_US
dc.typearticleen_US
dc.departmentAmasya Üniversitesien_US
dc.authoridkaur, manjit/0000-0001-6259-2046
dc.authoridAlshazly, Hammam/0000-0002-9942-8642
dc.authoridÖztürk, Şaban/0000-0003-2371-8173
dc.authoridChahar, Vijay Kumar/0000-0002-3460-6989
dc.identifier.volume70en_US
dc.identifier.issue3en_US
dc.identifier.startpage6239en_US
dc.identifier.endpage6255en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopus2-s2.0-85117082238en_US
dc.identifier.doi10.32604/cmc.2022.020682
dc.department-temp[Kalra, Minakshi] Govt Coll Bahadurgarh, Dept Comp Sci, Bahadurgarh 124507, India; [Kumar, Vijay] Natl Inst Technol Hamirpur, CSE Dept, Hamirpur 177005, India; [Kaur, Manjit] Bennett Univ, Sch Engn & Appl Sci, Greater Noida 201310, India; [Idris, Sahar Ahmed] King Khalid Univ, Coll Ind Engn, Abha, Saudi Arabia; [Ozturk, Saban] Amasya Univ, Dept Elect & Elect Engn, Amasya, Turkey; [Alshazly, Hammam] South Valley Univ, Fac Comp & Informat, Qena 83523, Egypten_US
dc.identifier.wosWOS:000707364500035en_US
dc.authorwosidkaur, manjit/J-2846-2019
dc.authorwosidAlshazly, Hammam/T-8666-2019
dc.authorwosidChahar, Vijay Kumar/A-2782-2015
dc.authorwosidÖztürk, Şaban/ABI-3936-2020


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