CLC number: TP391.9; TN929.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-06-02
Cited: 0
Clicked: 2298
Zhihao HE, Gang JIN, Yingjun WANG. A novel grey wolf optimizer and its applications in 5G frequency selection surface design[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(9): 1338-1353.
@article{title="A novel grey wolf optimizer and its applications in 5G frequency selection surface design",
author="Zhihao HE, Gang JIN, Yingjun WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="9",
pages="1338-1353",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100580"
}
%0 Journal Article
%T A novel grey wolf optimizer and its applications in 5G frequency selection surface design
%A Zhihao HE
%A Gang JIN
%A Yingjun WANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 9
%P 1338-1353
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100580
TY - JOUR
T1 - A novel grey wolf optimizer and its applications in 5G frequency selection surface design
A1 - Zhihao HE
A1 - Gang JIN
A1 - Yingjun WANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 9
SP - 1338
EP - 1353
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100580
Abstract: In fifth-generation wireless communication system (5G), more connections are built between metaheuristics and electromagnetic equipment design. In this paper, we propose a self-adaptive grey wolf optimizer (SAGWO) combined with a novel optimization model of a 5G frequency selection surface (FSS) based on FSS unit nodes. SAGWO includes three improvement strategies, improving the initial distribution, increasing the randomness, and enhancing the local search, to accelerate the convergence and effectively avoid local optima. In benchmark tests, the proposed optimizer performs better than the five other optimization algorithms: original grey wolf optimizer (GWO), genetic algorithm (GA), particle swarm optimizer (PSO), improved grey wolf optimizer (IGWO), and selective opposition based grey wolf optimization (SOGWO). Due to its global searchability, SAGWO is suitable for solving the optimization problem of a 5G FSS that has a large design space. The combination of SAGWO and the new FSS optimization model can automatically obtain the shape of the FSS unit with electromagnetic interference shielding capability at the center operating frequency. To verify the performance of the proposed method, a double-layer ring FSS is designed with the purpose of providing electromagnetic interference shielding features at 28 GHz. The results show that the optimized FSS has better electromagnetic interference shielding at the center frequency and has higher angular stability. Finally, a sample of the optimized FSS is fabricated and tested.
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