Full Text:   <123>

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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

Cited: 0

Clicked: 165

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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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。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Aljarah I, Ludwig SA, 2013. A new clustering approach based on glowworm swarm optimization. IEEE Congress on Evolutionary Computation, p.2642-2649.

[2]An D, Kim NH, Choi JH, 2015. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliab Eng Syst Safety, 133:223-236.

[3]Boursianis AD, Goudos SK, Yioultsis TV, et al., 2019. Low-cost dual-band E-shaped patch antenna for energy harvesting applications using grey wolf optimizer. 13th European Conf on Antennas and Propagation, p.1-5.

[4]Cai ZN, Gu JH, Luo J, et al., 2019. Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Syst Appl, 138:112814.

[5]Carrasco J, García S, Rueda MM, et al., 2020. Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput, 54:‍100665.

[6]Crevecoeur G, Sergeant P, Dupré L, et al., 2010. A two-level genetic algorithm for electromagnetic optimization. IEEE Trans Magn, 46(7):2585-2595.

[7]Dehghani M, Seifi A, Riahi-Madvar H, 2019. Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization. J Hydrol, 576:698-725.

[8]Dhargupta S, Ghosh M, Mirjalili S, et al., 2020. Selective opposition based grey wolf optimization. Expert Syst Appl, 151:113389.

[9]Donyaii A, Sarraf A, Ahmadi H, 2020. Water reservoir multiobjective optimal operation using grey wolf optimizer. Shock Vibr, 2020:8870464.

[10]Ge YH, Esselle KP, Hao Y, 2007. Design of low-profile high-gain EBG resonator antennas using a genetic algorithm. IEEE Antenn Wirel Propag Lett, 6:480-483.

[11]Genovesi S, Mittra R, Monorchio A, et al., 2006. Particle swarm optimization for the design of frequency selective surfaces. IEEE Antenn Wirel Propag Lett, 5:277-279.

[12]Goudos SK, Yioultsis TV, Boursianis AD, et al., 2019. Application of new hybrid Jaya grey wolf optimizer to antenna design for 5G communications systems. IEEE Access, 7:71061-71071.

[13]Gupta S, Deep K, 2019. A novel random walk grey wolf optimizer. Swarm Evol Comput, 44:101-112.

[14]Gutiérrez AL, Lanza M, Barriuso I, et al., 2011. Multilayer FSS optimizer based on PSO and CG-FFT. IEEE Int Symp on Antennas and Propagation, p.2661-2664.

[15]Heidari AA, Mirjalili S, Faris H, et al., 2019. Harris hawks optimization: algorithm and applications. Fut Gener Comput Syst, 97:849-872.

[16]Hu J, Chen HL, Heidari AA, et al., 2021. Orthogonal learning covariance matrix for defects of grey wolf optimizer: insights, balance, diversity, and feature selection. Knowl-Based Syst, 213:106684.

[17]Khan SU, Rahim MKA, Ali L, 2018. Correction of array failure using grey wolf optimizer hybridized with an interior point algorithm. Front Inform Technol Electron Eng, 19(9):1191-1202.

[18]Li D, Li TW, Hao R, et al., 2017. A low-profile broadband bandpass frequency selective surface with two rapid band edges for 5G near-field applications. IEEE Trans Electromagn Compat, 59(2):670-676.

[19]Li D, Li TW, Li EP, et al., 2018. A 2.5-D angularly stable frequency selective surface using via-based structure for 5G EMI shielding. IEEE Trans Electromagn Compat, 60(3):768-775.

[20]Li Q, Chen HL, Huang H, et al., 2017. An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med, 2017:9512741.

[21]Liu Y, Zhang YM, Gao S, 2020. Pattern synthesis of antenna arrays using dynamic cooperative grey wolf optimizer algorithm. IEEE 10th Int Conf on Electronics Information and Emergency Communication, p.186-189.

[22]Mirjalili S, 2016. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neur Comput Appl, 27(4):1053-1073.

[23]Mirjalili S, Mirjalili SM, Lewis A, 2014. Grey wolf optimizer. Adv Eng Softw, 69:46-61.

[24]Mohanty S, Subudhi B, Ray PK, 2016. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans Sustain Energy, 7(1):181-188.

[25]Nadimi-Shahraki MH, Taghian S, Mirjalili S, 2021. An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl, 166:113917.

[26]Parker EA, Chuprin AD, Batchelor JC, et al., 2001. GA optimisation of crossed dipole FSS array geometry. Electron Lett, 37(16):996-997.

[27]Paul GS, Mandal K, Das P, 2021. Low profile polarization-insensitive wide stop-band frequency selective surface with effective electromagnetic shielding. Int J RF Microw Comput Aided Eng, 31(3):e22527.

[28]Peng T, Zhou BH, 2019. Hybrid bi-objective gray wolf optimization algorithm for a truck scheduling problem in the automotive industry. Appl Soft Comput, 81:105513.

[29]Phan HD, Ellis K, Barca JC, et al., 2020. A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms. Neur Comput Appl, 32(2):567-588.

[30]Saxena P, Kothari A, 2016. Optimal pattern synthesis of linear antenna array using grey wolf optimization algorithm. Int J Antenn Propag, 2016:1205970.

[31]Shakarami MR, Davoudkhani FI, 2016. Wide-area power system stabilizer design based on grey wolf optimization algorithm considering the time delay. Electr Power Syst Res, 133:149-159.

[32]Tu JZ, Chen HL, Wang MJ, et al., 2021. The colony predation algorithm. J Bion Eng, 18(3):674-710.

[33]Villegas FJ, Cwik T, Rahmat-Samii Y, et al., 2004. A parallel electromagnetic genetic-algorithm optimization (EGO) application for patch antenna design. IEEE Trans Antenn Propag, 52(9):2424-2435.

[34]Wang GG, 2018. Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput, 10(2):151-164.

[35]Wang GG, Deb S, Cui Z, 2019. Monarch butterfly optimization. Neur Comput Appl, 31(7):1995-2014.

[36]Yu HL, Song JM, Chen CC, et al., 2022. Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm. Eng Appl Artif Intell, 109:104653.

[37]Zou DX, Liu HK, Gao LQ, et al., 2011. An improved differential evolution algorithm for the task assignment problem. Eng Appl Artif Intell, 24(4):616-624.

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