An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm

Liyuan Xue1; Bo Liu1; Yang Yu2; Qingsha S. Cheng3; Muhammad Imran1; Tianrui Qiao3

2022

10.1109/TMTT.2022.3219072

IEEE Transactions on Microwave Theory and Techniques   影响因子和分区

1557-9670

1557-9670

In resonator-coupled bandpass filter 3D design, it is a routine that the filter optimization methods are guided/supervised by designers' experience to carry out an iterative design optimization process. To realize automated or unsupervised filter 3D design optimization, a new method, called hybrid surrogate model-assisted evolutionary algorithm for filter optimization (H-SMEAFO), is proposed. H-SMEAFO aims to automatically obtain a highly optimal filter 3D design without designers' interaction (i.e., unsupervised) and is also not restricted to certain kinds of filter structures. In H-SMEAFO, the key innovations include a hybrid response feature-based objective function and a hybrid surrogate model-assisted global optimization algorithm; both are designed bespoke for filter design landscape characteristics. The performance of H-SMEAFO is demonstrated by an 8th-order dual-band waveguide filter with four transmission zeros and a 6th-order waveguide filter with two transmission zeros, for which, unsupervised design optimization does not appear to be possible using existing methods. Numerical results show the effectiveness and advantages of H-SMEAFO.

Design optimization differential evolution (DE) filter response features microwave filters objective function surrogate modeling

SCI ; EI

英语

其他

Engineering

Engineering, Electrical & Electronic

WOS:000890814200001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20225113271752

Bandpass filters ; Evolutionary algorithms ; Global optimization ; Iterative methods ; Linear programming ; Three dimensional computer graphics ; Three dimensional displays ; Waveguide filters

Electric Filters:703.2 ; Waveguides:714.3 ; Computer Peripheral Equipment:722.2 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Optimization Techniques:921.5 ; Numerical Methods:921.6

ENGINEERING

IEEE

1.James Watt School of Engineering, University of Glasgow, Glasgow, U.K.
2.Electronic and System Engineering, School of Electrical, University of Birmingham, Birmingham, U.K.
3.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Liyuan Xue,Bo Liu,Yang Yu,et al. An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm[J]. IEEE Transactions on Microwave Theory and Techniques,2022,PP(99):1-12.

Liyuan Xue,Bo Liu,Yang Yu,Qingsha S. Cheng,Muhammad Imran,&Tianrui Qiao.(2022).An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm.IEEE Transactions on Microwave Theory and Techniques,PP(99),1-12.

Liyuan Xue,et al."An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm".IEEE Transactions on Microwave Theory and Techniques PP.99(2022):1-12.