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CLC number: TP391.9; TN929.5

On-line Access: 2022-09-21

Received: 2021-12-22

Revision Accepted: 2022-09-21

Crosschecked: 2022-06-02

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Citations:  Bibtex RefMan EndNote GB/T7714


Yingjun WANG


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.9 P.1338-1353


A novel grey wolf optimizer and its applications in 5G frequency selection surface design

Author(s):  Zhihao HE, Gang JIN, Yingjun WANG

Affiliation(s):  National Engineering Research Center of Novel Equipment for Polymer Processing, South China University of Technology, Guangzhou 510641, China; more

Corresponding email(s):   wangyj84@scut.edu.cn

Key Words:  Grey wolf optimizer, Fifth-generation wireless communication system (5G), Frequency selection surface, Shape optimization

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.

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%T A novel grey wolf optimizer and its applications in 5G frequency selection surface design
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%A Gang JIN
%A Yingjun WANG
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A1 - Zhihao HE
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DOI - 10.1631/FITEE.2100580

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.


摘要:第五代无线通信系统(5G)的发展使元启发算法与电磁设备的设计过程结合得更为紧密。本文提出一种自适应灰狼优化器(SAGWO),并将其与一种基于单元节点的5G频率选择面(FSS)优化模型相结合。SAGWO包含3种改进策略:改进初始头狼的分配,增加随机探索能力和增强局部搜索能力,以加快收敛速度,有效避免局部最优。在基准函数测试中,SAGWO优于其他5种优化算法:原始灰狼优化器(GWO)、遗传算法(GA)、粒子群优化器(PSO)、改进灰狼优化算法(IGWO)和基于选择性对抗的灰狼优化算法(SOGWO)。因为SAGWO具有良好全局寻优能力,所以SAGWO适用于解决具有较大设计空间的5G FSS优化问题。将SAGWO与新的FSS优化模型相结合,能自动生成在中心工作频率处具有电磁屏蔽能力的FSS结构。为验证所提方法,本文设计了在28 GHz处具有电磁屏蔽能力的双层环形FSS。结果表明,优化后的FSS在中心频率处具有较好电磁干扰屏蔽能力和较高角稳定性。最后,制作并测试了优化后的FSS样品。


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