Feature-based Surrogate-assisted Harris Hawks Optimization Algorithm for Microwave Filters

Jiao，Yaxi; Zhang，Zhen; Yu，Yang; Cheng，Qingsha S.

10.23919/ACES-China52398.2021.9582008

2021-07-28

978-1-6654-3377-8

2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings

1-2

28-31 July 2021

Chengdu, China

This paper proposes a feature-based surrogate-assisted Harris hawks optimization (FBSAHHO) algorithm for microwave filters. The method is constructed in three steps. Firstly, the vector fitting technology is used to extract the features (parameters of a transfer function) of the electromagnetic (EM) responses. Secondly, artificial neural networks are trained to establish the relationship between design parameters and the extracted features. Thirdly, a surrogate is generated using the filter frequency responses recovered by substituting obtained features into the transfer function. Lastly, Harris hawks optimization algorithm is adopted to find optimal solutions using the surrogate,. A fourth-order coaxial cavity filter is used to verify the proposed method. Combining Harris hawks optimization with surrogate-assisted modeling algorithm demonstrates faster and better convergence than other surrogate-assisted and direct optimization algorithms for filter design.

artificial neural network microwave filter design optimization surrogate

第一

英语

EI

20214711199009

Computational electromagnetics ; Frequency response ; Microwave filters ; Optimization ; Transfer functions

Electricity and Magnetism:701 ; Electric Filters:703.2 ; Mathematics:921 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

2-s2.0-85119356676

Scopus

University Key Laboratory of Advanced Wireless Communications of Guangdong Province,Southern University of Science and Technology,Department of Electrical and Electronic Engineering,Shenzhen,518055,China

Jiao，Yaxi,Zhang，Zhen,Yu，Yang,et al. Feature-based Surrogate-assisted Harris Hawks Optimization Algorithm for Microwave Filters[C],2021:1-2.