An unsupervised microwave filter design optimization method based on a hybrid surrogate model-assisted evolutionary algorithm

Xue, L., Liu, B. , Yu, Y., Cheng, Q. S., Imran, M. and Qiao, T. (2023) An unsupervised microwave filter design optimization method based on a hybrid surrogate model-assisted evolutionary algorithm. IEEE Transactions on Microwave Theory and Techniques, 71(3), pp. 1159-1170. (doi: 10.1109/TMTT.2022.3219072)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and XUE, Liyuan and Liu, Professor Bo
• Authors: Xue, L., Liu, B., Yu, Y., Cheng, Q. S., Imran, M., and Qiao, T.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Microwave Theory and Techniques
• Publisher: IEEE
• ISSN: 0018-9480
• ISSN (Online): 1557-9670
• Published Online: 23 November 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Microwave Theory and Techniques 71(3): 1159-1170
• Publisher Policy: Reproduced in accordance with the publisher copyright policy