Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Oluwatayomi Rereloluwa Adegboye; Afi Kekeli Feda; Meshack Magaji Ishaya; Ephraim Bonah Agyekum; Ki-Chai Kim; Wulfran Fendzi Mbasso; Salah Kamel

doi:10.1016/j.heliyon.2023.e21596

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

Heliyon. 2023 Nov 4;9(11):e21596. doi: 10.1016/j.heliyon.2023.e21596. eCollection 2023 Nov.

Authors

Oluwatayomi Rereloluwa Adegboye¹, Afi Kekeli Feda², Meshack Magaji Ishaya³, Ephraim Bonah Agyekum⁴, Ki-Chai Kim⁵, Wulfran Fendzi Mbasso⁶, Salah Kamel⁷

Affiliations

¹ Management Information Systems, University of Mediterranean Karpasia, Nicosia, Mersin, 10, Turkey.
² Management Information System Department, European University of Lefke, Mersin, 10, Turkey.
³ Electrical and Electronics Engineering Department, Cyprus International University, Mersin, 10, Turkey.
⁴ Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris, 19 Mira Street, Yeltsin, Ekaterinburg, 620002, Russia.
⁵ Department of Electrical Engineering, Yeungnam University, Gyeongsan, 38541, South Korea.
⁶ Laboratory of Technology and Applied Sciences, University Institute of Technology, University of Douala, PO Box: 8698, Douala, Cameroon.
⁷ Electrical Engineering Department, Faculty of Engineering, Aswan University, 81542, Aswan, Egypt.

Abstract

This work proposed a new method to optimize the antenna S-parameter using a Golden Sine mechanism-based Honey Badger Algorithm that employs Tent chaos (GST-HBA). The Honey Badger Algorithm (HBA) is a promising optimization method that similar to other metaheuristic algorithms, is prone to premature convergence and lacks diversity in the population. The Honey Badger Algorithm is inspired by the behavior of honey badgers who use their sense of smell and honeyguide birds to move toward the honeycomb. Our proposed approach aims to improve the performance of HBA and enhance the accuracy of the optimization process for antenna S-parameter optimization. The approach we propose in this study leverages the strengths of both tent chaos and the golden sine mechanism to achieve fast convergence, population diversity, and a good tradeoff between exploitation and exploration. We begin by testing our approach on 20 standard benchmark functions, and then we apply it to a test suite of 8 S-parameter functions. We perform tests comparing the outcomes to those of other optimization algorithms, the result shows that the suggested algorithm is superior.