TY - JOUR

T1 - Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

AU - Adegboye, Oluwatayomi Rereloluwa

AU - Feda, Afi Kekeli

AU - Ishaya, Meshack Magaji

AU - Agyekum, Ephraim

AU - Kim, Ki-Chai

AU - Mbasso, Wulfran Fendzi

AU - Kamel, Salah

PY - 2023/11/1

Y1 - 2023/11/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85176105452

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001112373600001

U2 - 10.1016/j.heliyon.2023.e21596

DO - 10.1016/j.heliyon.2023.e21596

M3 - Article

VL - 9

JO - Heliyon

JF - Heliyon

SN - 2405-8440

IS - 11

M1 - e21596

ER -