TY - JOUR
T1 - An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model Assisted Optimization Technique
AU - Liu, Bo
AU - Akinsolu, Mobayode O.
AU - Song, Chaoyun
AU - Hua, Qiang
AU - Excell, Peter
AU - Xu, Qian
AU - Huang, Yi
AU - Imran, Muhammad Ali
N1 - Funding Information:
Manuscript received December 6, 2019; revised June 19, 2020; accepted July 21, 2020. Date of publication January 18, 2021; date of current version April 7, 2021. This work was supported by MathWorks Development Collaboration Research Grant. (Corresponding author: Bo Liu.) Bo Liu and Muhammad Ali Imran are with the School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K. (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 1963-2012 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4
Y1 - 2021/4
N2 - Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.
AB - Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.
KW - 5G base station antenna
KW - Gaussian process
KW - antenna design
KW - complex antenna
KW - computationally expensive optimization
KW - differential evolution
KW - radial basis function
KW - surrogate model
UR - http://www.scopus.com/inward/record.url?scp=85099729727&partnerID=8YFLogxK
U2 - 10.1109/TAP.2021.3051034
DO - 10.1109/TAP.2021.3051034
M3 - Article
AN - SCOPUS:85099729727
SN - 0018-926X
VL - 69
SP - 2302
EP - 2315
JO - IEEE Transactions on Antennas and Propagation
JF - IEEE Transactions on Antennas and Propagation
IS - 4
ER -