A Hybrid QOGWO-GPR Algorithm for Antenna Optimization

Optimization of antenna performance is a non-linear, multi-dimensional and complex issue, which entails a significant investment of time and labor. In this paper, a hybrid algorithm of quasi-opposition grey wolf optimization (QOGWO) and Gaussian process regression (GPR) model is proposed for antenna...

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Bibliographic Details
Published in:Applied Computational Electromagnetics Society journal 2023-09, Vol.38 (9), p.674-680
Main Authors: Zhu, Hao-Yun, Qian, Jia-Wei, Tang, Xiao-Hui, Li, Wei-Dong
Format: Article
Language:English
Subjects:
Algorithms
Animal populations
Antennas
Gaussian process
Monopole antennas
Optimization
Regression models
Ultrawideband
Wolves
Online Access:Get full text
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Summary:Optimization of antenna performance is a non-linear, multi-dimensional and complex issue, which entails a significant investment of time and labor. In this paper, a hybrid algorithm of quasi-opposition grey wolf optimization (QOGWO) and Gaussian process regression (GPR) model is proposed for antenna optimization. The QOGWO is prone to global optimality, high precision for complex problems, and fast convergence rate at the later stage. The GPR model can reduce time cost of antenna samples generation. After being optimized by the proposed approach, a stepped ultrawideband monopole antenna and a dual-band MIMO antenna for WLAN can achieve wider bandwidth and higher gain or isolation at low time cost, compared to other intelligent algorithms and published literatures.
ISSN:1054-4887
1943-5711
DOI:10.13052/2023.ACES.J.380906