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

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

doi:10.1016/j.heliyon.2023.e21596

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…It is also worthwhile to explore certain modified algorithms that exhibit exceptional performance, such as Modified Social Group Optimization (MSGO) 16 , Chaotic Vortex Search Algorithm (VSA) 17 , Modified Marine Predators Algorithm (MMPA) 18 , and Hybrid Binary Dwarf Mongoose Optimization Algorithm (BDMSAO) 19 . They have found practical applications in various domains, including parameter identification 20 , feature selection 21 , 22 , Antenna Optimization 23 , Image Segmentation 24 , 25 , demand prediction 26 , Reliability-Based Design 27 , 28 , constrained optimization problems 21 , 22 . These algorithms, however, share several challenges, such as a propensity to get trapped in local optimal solutions, sluggish convergence rate, and limited precision in identifying the optimal solution 29 .…”

Section: Introductionmentioning

confidence: 99%

Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization

Adegboye,

Feda,

Ojekemi

et al. 2024

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The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it’s called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.

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