Open AccessPareto‐optimal design of UHF antenna using modified non‐dominated sorting genetic algorithm II
Author(s) -
Bin Feng,
Wang Feng,
Chen She,
Sun Qiuqin,
Zhong Lipeng,
Lin Shu
Publication year - 2020
Publication title -
iet microwaves, antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.555
H-Index - 69
eISSN - 1751-8733
pISSN - 1751-8725
DOI - 10.1049/iet-map.2020.0121
Subject(s) - genetic algorithm , antenna (radio) , sorting , crossover , ultra high frequency , multi objective optimization , return loss , electronic engineering , computer science , mathematical optimization , mathematics , algorithm , telecommunications , engineering , artificial intelligence
In this study, the ultra‐high‐frequency (UHF) antenna for partial discharge (PD) detection is optimised to simultaneously satisfy the requirements of low return loss and high fidelity factor (FF) in the frequency band of interest by using the modified non‐dominated sorting genetic algorithm II (MNSGA‐II). Based on the labour division strategy, the MNSGA‐II adopts an adaptive crossover and mutation possibilities instead of the fixed ones, resulting in the significant improvement of convergence rate and exploration ability. One of the Pareto‐optimal solutions is presented as the authors’ proposed UHF antenna, whose performance is compared with those of both the antenna optimised by genetic algorithm and the existing wideband antennas. The experimental results show that the proposed antenna with a compact size of 0.201 λ L × 0.198 λ L realises the reflection coefficient less than −10 dB from 490 MHz to 1.52 GHz, and its FFs in the face‐to‐face and side‐by‐side scenarios are 0.897 and 0.845, respectively. Furthermore, the simulation results reveal that the high FF of antenna greatly increases the accuracy of PD source localisation. It indicates that the MNSGA‐II provides an excellent solution to the multiobjective optimisation problems in antenna design.