An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis

Fu, K; Cai, X; Yuan, B; Yang, Y; Yao, X

An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis

Fu, K Cai, X Yuan, B Yang, Y

Yao, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Antennas and Propagation, 2022, 70, (7), pp. 4977-4984
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Fu, K
dc.contributor.author	Cai, X
dc.contributor.author	Yuan, B
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-7439-2156
dc.contributor.author	Yao, X
dc.date.accessioned	2023-03-20T01:11:48Z
dc.date.available	2023-03-20T01:11:48Z
dc.date.issued	2022-07-01
dc.identifier.citation	IEEE Transactions on Antennas and Propagation, 2022, 70, (7), pp. 4977-4984
dc.identifier.issn	0018-926X
dc.identifier.issn	1558-2221
dc.identifier.uri	http://hdl.handle.net/10453/167681
dc.description.abstract	By virtue of the prediction abilities of machine learning (ML) methods, the ML-assisted evolutionary algorithm has been treated as an efficient solution for antenna design automation. This article presents an efficient ML-based surrogate-assisted particle swarm optimization (SAPSO). The proposed algorithm closely combines the particle swarm optimization (PSO) with two ML-based approximation models. Then, a novel mixed prescreening (mixP) strategy is proposed to pick out promising individuals for full-wave electromagnetic (EM) simulations. As the optimization procedure progresses, the ML models are dynamically updated once new training data are obtained. Finally, the proposed algorithm is verified by three real-world antenna examples. The results show that the proposed SAPSO-mixP can find favorable results with a much smaller number of EM simulations than other methods.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Antennas and Propagation
dc.relation.isbasedon	10.1109/TAP.2022.3153080
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-20T01:11:47Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	7

Abstract:

By virtue of the prediction abilities of machine learning (ML) methods, the ML-assisted evolutionary algorithm has been treated as an efficient solution for antenna design automation. This article presents an efficient ML-based surrogate-assisted particle swarm optimization (SAPSO). The proposed algorithm closely combines the particle swarm optimization (PSO) with two ML-based approximation models. Then, a novel mixed prescreening (mixP) strategy is proposed to pick out promising individuals for full-wave electromagnetic (EM) simulations. As the optimization procedure progresses, the ML models are dynamically updated once new training data are obtained. Finally, the proposed algorithm is verified by three real-world antenna examples. The results show that the proposed SAPSO-mixP can find favorable results with a much smaller number of EM simulations than other methods.

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http://hdl.handle.net/10453/167681