Pioneer Pareto artificial bee colony algorithm for three-dimensional objective space optimization of composite-based layered radar absorber

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dc.authorid0000-0002-7687-9061en_US
dc.contributor.authorToktaş, Abdurrahim
dc.contributor.authorÜstün, Deniz
dc.contributor.authorErdoğan, Nursev
dc.date.accessioned2020-12-23T13:03:20Z
dc.date.available2020-12-23T13:03:20Z
dc.date.issued2020en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000582762000097en_US
dc.description.abstractA three-dimensional objective space (3DOS) optimization strategy using an enhanced multi-objective artificial bee colony (ABC) algorithm for the design optimization of layered radar absorbing material (LRAM) is presented in this study. The multi-objective exploitation ability of ABC is improved with regard to the convergence and diversity by integrating a pioneer Pareto (PP) solution to the onlooker bee phase, which is selected from the Pareto optimal set. Initially, the performance of PP-ABC is successfully verified by a comparison with ABC and the well-known multi-objective counterparts like particle swarm optimization (PSO) and differential evolution (DE) algorithms. The comparison is carried out through five multi-objective benchmark functions with respect to three favorable and reliable multi-objective indicators such as hypervolume (HV), HV ratio and Pareto sets proximity (PSP). The employed three objective functions to be the dimensions of 3DOS are weighted bandwidth-based total reflection coefficient involving sub-reflection waves of a wide oblique incident angular range 0 degrees-75 degrees, the total thickness and the number of layers. By using PP-ABC, a 3D designed LRAM operating at a large frequency band of 2-18 GHz is then designed for synchronously minimizing the three objective vectors by finding out the design variables: thickness and material types. Meanwhile, the material types of the proposed LRAM are optimally picked up from a composite material database with 51 specimens from 9 previously reported studies (51 /9#database). In order to point out the effectiveness of the proposed 3DOS optimization strategy, three LRAMs are also compared with respective reported designs whose material type is selected from a database with 6 specimens (6/1#database). The results show that the proposed LRAMs are hence the global optimal designs in terms of all objective functions thanks to the proposed 3DOS optimization strategy based on PP-ABC.en_US
dc.description.sponsorshipThis research work was financially supported by Coordinator of Scientific Research Projects of Karamanoglu Mehmetbey University (KMU-BAP), Turkey under project number: 03-M-20.en_US
dc.identifier.citationToktas, A., Üstün, D., Erdogan, N. (2020). Pioneer Pareto artificial bee colony algorithm for three-dimensional objective space optimization of composite-based layered radar absorber. Applied Soft Computing, 96 . doi:10.1016/j.asoc.2020.106696en_US
dc.identifier.doi10.1016/j.asoc.2020.106696
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85090717384
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2020.106696
dc.identifier.urihttps://hdl.handle.net/11492/3735
dc.identifier.volume96en_US
dc.identifier.wosWOS:000582762000097
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorToktaş, Abdurrahim
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.journalApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMetaheuristic Optimization Algorithmen_US
dc.subjectMulti-Objective Optimizationen_US
dc.subjectEnhanced Algorithmen_US
dc.subjectPareto Optimalityen_US
dc.subjectRadar Absorbing Material (RAM)en_US
dc.subjectLayered RAMen_US
dc.titlePioneer Pareto artificial bee colony algorithm for three-dimensional objective space optimization of composite-based layered radar absorberen_US
dc.typeArticle

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