An efficient method for antenna design based on a self-adaptive bayesian neural network assisted global optimization technique

Liu, Y. , Liu, B. , Ur Rehman, M. , Imran, M. A. , Akinsolu, M. O., Excell, P. and Hua, Q. (2022) An efficient method for antenna design based on a self-adaptive bayesian neural network assisted global optimization technique. IEEE Transactions on Antennas and Propagation, 70(12), pp. 11375-11388. (doi: 10.1109/TAP.2022.3211732)

Abstract

Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Ur Rehman, Dr Masood and Liu, Yushi and Liu, Professor Bo
• Authors: Liu, Y., Liu, B., Ur Rehman, M., Imran, M. A., Akinsolu, M. O., Excell, P., and Hua, Q.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Antennas and Propagation
• Publisher: IEEE
• ISSN: 0018-926X
• ISSN (Online): 1558-2221
• Published Online: 10 October 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Antennas and Propagation 70(12): 11375-11388
• Publisher Policy: Reproduced in accordance with the publisher copyright policy