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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.

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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.




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