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On-line Access: 2022-09-21

Received: 2021-09-03

Revision Accepted: 2022-09-21

Crosschecked: 2022-04-19

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.9 P.1390-1406


Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization

Author(s):  Jian DONG, Xia YUAN, Meng WANG

Affiliation(s):  School of Computer Science and Engineering, Central South University, Changsha 410083, China

Corresponding email(s):   dongjian@csu.edu.cn, yuan0927@csu.edu.cn, mwang2@csu.edu.cn

Key Words:  Antenna topology optimization, Multi-objective grey wolf optimizer, High-dimensional mixed variables, Fast design

Jian DONG, Xia YUAN, Meng WANG. Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(9): 1390-1406.

@article{title="Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization",
author="Jian DONG, Xia YUAN, Meng WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization
%A Jian DONG
%A Meng WANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 9
%P 1390-1406
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100420

T1 - Competitive binary multi-objective grey wolf optimizer for fast compact antenna topology optimization
A1 - Jian DONG
A1 - Xia YUAN
A1 - Meng WANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 9
SP - 1390
EP - 1406
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100420

We propose a competitive binary multi-objective grey wolf optimizer (CBMOGWO) to reduce the heavy computational burden of conventional multi-objective antenna topology optimization problems. This method introduces a population competition mechanism to reduce the burden of electromagnetic (EM) simulation and achieve appropriate fitness values. Furthermore, we introduce a function of cosine oscillation to improve the linear convergence factor of the original binary multi-objective grey wolf optimizer (BMOGWO) to achieve a good balance between exploration and exploitation. Then, the optimization performance of CBMOGWO is verified on 12 standard multi-objective test problems (MOTPs) and four multi-objective knapsack problems (MOKPs) by comparison with the original BMOGWO and the traditional binary multi-objective particle swarm optimization (BMOPSO). Finally, the effectiveness of our method in reducing the computational cost is validated by an example of a compact high-isolation dual-band multiple-input multiple-output (MIMO) antenna with high-dimensional mixed design variables and multiple objectives. The experimental results show that CBMOGWO reduces nearly half of the computational cost compared with traditional methods, which indicates that our method is highly efficient for complex antenna topology optimization problems. It provides new ideas for exploring new and unexpected antenna structures based on multi-objective evolutionary algorithms (MOEAs) in a flexible and efficient manner.




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


[1]Aldhafeeri A, Rahmat-Samii Y, 2019. Brain storm optimization for electromagnetic applications: continuous and discrete. IEEE Trans Antenn Propag, 67(4):2710-2722.

[2]Balanis CA, 2016. Antenna Theory: Analysis and Design (4th Ed.). John Wiley & Sons, Hoboken, USA.

[3]Bataineh M, Marler T, 2017. Neural network for regression problems with reduced training sets. Neur Netw, 95:1-9.

[4]Bin F, Wang F, Chen S, et al., 2020. Pareto-optimal design of UHF antenna using modified non-dominated sorting genetic algorithm II. IET Microw Antenn Propag, 14(12):‍1404-1410.

[5]Carvalho R, Saldanha RR, Gomes BN, et al., 2012. A multi-objective evolutionary algorithm based on decomposition for optimal design of Yagi-Uda antennas. IEEE Trans Magn, 48(2):803-806.

[6]Chen YK, Wang CF, 2012. Synthesis of reactively controlled antenna arrays using characteristic modes and DE algorithm. IEEE Antenn Wirel Propag Lett, 11:385-388.

[7]Chirikov R, Rocca P, Manica L, et al., 2013. Innovative GA-based strategy for polyomino tiling in phased array design. Proc 7th European Conf on Antennas and Propagation, p.2216-2219.

[8]Coello CAC, Pulido GT, Lechuga MS, 2004. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput, 8(3):256-279.

[9]Dhaliwal BS, Pattnaik SS, 2017. BFO-ANN ensemble hybrid algorithm to design compact fractal antenna for rectenna system. Neur Comput Appl, 28(1):917-928.

[10]Ding K, Gao C, Qu DX, et al., 2017. Compact broadband MIMO antenna with parasitic strip. IEEE Antenn Wirel Propag Lett, 16:2349-2353.

[11]Dong J, Li QQ, Deng LW, 2018. Design of fragment-type antenna structure using an improved BPSO. IEEE Trans Antenn Propag, 66(2):564-571.

[12]Dong J, Li YJ, Wang M, 2019a. Fast multi-objective antenna optimization based on RBF neural network surrogate model optimized by improved PSO algorithm. Appl Sci, 9(13):2589.

[13]Dong J, Qin WW, Wang M, 2019b. Fast multi-objective optimization of multi-parameter antenna structures based on improved BPNN surrogate model. IEEE Access, 7:77692-77701.

[14]Du YJ, Wu XP, Sidén J, et al., 2020. Design of ultra-wideband antenna with high-selectivity band notches using fragment-type etch pattern. Microw Opt Technol Lett, 62(2):912-918.

[15]Emary E, Zawbaa HM, Hassanien AE, 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172:371-381.

[16]Gupta N, Saxena J, Bhatia KS, 2020. Optimized metamaterial-loaded fractal antenna using modified hybrid BF-PSO algorithm. Neur Comput Appl, 32(11):7153-7169.

[17]Ishibuchi H, Masuda H, Tanigaki Y, et al., 2015. Modified distance calculation in generational distance and inverted generational distance. Proc 8th Int Conf on Evolutionary Multi-Criterion Optimization, p.110-125.

[18]Jehangir SS, Sharawi MS, 2020. A compact single-layer four-port orthogonally polarized Yagi-like MIMO antenna system. IEEE Trans Antenn Propag, 68(8):6372-6377.

[19]Jia XN, Lu GZ, 2019. A hybrid Taguchi binary particle swarm optimization for antenna designs. IEEE Antenn Wirel Propag Lett, 18(8):1581-1585.

[20]Kaur J, Nitika, Panwar R, 2019. Design and optimization of a dual-band slotted microstrip patch antenna using differential evolution algorithm with improved cross polarization characteristics for wireless applications. J Electromagn Waves Appl, 33(11):1427-1442.

[21]Kim Y, Walton EK, 2006. Automobile conformal antenna design using non-dominated sorting genetic algorithm (NSGA). IEE Proc Microw Antenn Propag, 153(6):‍579-582.

[22]Koziel S, Bekasiewicz A, 2016. Fast multi-objective surrogate-assisted design of multi-parameter antenna structures through rotational design space reduction. IET Microw Antenn Propag, 10(6):624-630.

[23]Koziel S, Ogurtsov S, 2013. Multi-objective design of antennas using variable-fidelity simulations and surrogate models. IEEE Trans Antenn Propag, 61(12):5931-5939.

[24]Kumar J, 2016. Compact MIMO antenna. Microw Opt Technol Lett, 58(6):1294-1298.

[25]Li CM, Li Z, Jun X, et al., 2020. The impact of data quality on neural network models. Proc Int Conf on Cyber Security Intelligence and Analytics, p.657-665.

[26]Li QQ, Chu QX, Chang YL, et al., 2020a. Tri-objective compact log-periodic dipole array antenna design using MOEA/D-GPSO. IEEE Trans Antenn Propag, 68(4):2714-2723.

[27]Li QQ, Chu QX, Chang YL, 2020b. Design of compact high-isolation MIMO antenna with multiobjective mixed optimization algorithm. IEEE Antenn Wirel Propag Lett, 19(8):1306-1310.

[28]Li R, Xu L, Hu W, et al., 2017. Low-cross-polarisation synthesis of conformal antenna arrays using a balanced dynamic differential evolution algorithm. IET Microw Antenn Propag, 11(13):1853-1860.

[29]Li YL, Shao W, You L, et al., 2013. An improved PSO algorithm and its application to UWB antenna design. IEEE Antenn Wirel Propag Lett, 12:1236-1239.

[30]Lin ZQ, Yao ML, Shen XW, 2012. Sidelobe reduction of the low profile multi-subarray antenna by genetic algorithm. AEU-Int J Electron Commun, 66(2):133-139.

[31]Marler RT, Arora JS, 2004. Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim, 26(6):369-395.

[32]Marler RT, Arora JS, 2009. Multi-objective Optimization: Concepts and Methods for Engineering. VDM Publishing.

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

[34]Mirjalili S, Mirjalili SM, Yang XS, 2014b. Binary bat algorithm. Neur Comput Appl, 25(3-4):663-681.

[35]Mirjalili S, Saremi S, Mirjalili SM, et al., 2016. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl, 47:106-119.

[36]Panduro MA, Covarrubias DH, Brizuela CA, et al., 2005. A multi-objective approach in the linear antenna array design. AEU-Int J Electron Commun, 59(4):205-212.

[37]Panduro MA, Brizuela CA, Garza J, et al., 2013. A comparison of NSGA-II, DEMO, and EM-MOPSO for the multi-objective design of concentric rings antenna arrays. J Electromagn Waves Appl, 27(9):1100-1113.

[38]Pietrenko-Dabrowska A, Koziel S, Al-Hasan M, 2020. Cost-efficient bi-layer modeling of antenna input characteristics using gradient Kriging surrogates. IEEE Access, 8:140831-140839.

[39]Ren ZY, Zhao AP, 2019. Dual-band MIMO antenna with compact self-decoupled antenna pairs for 5G mobile applications. IEEE Access, 7:82288-82296.

[40]Sharawi MS, Numan AB, Khan MU, et al., 2012. A dual-element dual-band MIMO antenna system with enhanced isolation for mobile terminals. IEEE Antenn Wirel Propag Lett, 11:1006-1009.

[41]Tian Y, Cheng R, Zhang XY, et al., 2017. PlatEMO: a MATLAB platform for evolutionary multi-objective optimization [Educational Forum]. IEEE Comput Intell Mag, 12(4):73-87.

[42]Zhang L, Wang X, He SQ, 2019. Topology optimization of antenna for maximum bandwidth design. Proc IEEE Int Conf on Computational Electromagnetics, p.1-3.

[43]Zhang QF, Li H, 2007. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput, 11(6):712-731.

[44]Zhang QF, Zhou AM, Zhao SZ, et al., 2009. Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition. Technical Report CES-487.

[45]Zhu SH, Yang XS, Wang J, et al., 2019. Design of MIMO antenna isolation structure based on a hybrid topology optimization method. IEEE Trans Antenn Propag, 67(10):6298-6307.

[46]Zitzler E, Thiele L, 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput, 3(4):257-271.

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