Accelerated Multi-Objective Design Optimization of Antennas By Surrogate Modeling and Domain Segmentation

Koziel, Slawomir1,2; Bekasiewicz, Adrian1,2; Cheng, Qingsha S.3; Li, Song.3,4

10.23919/EuCAP.2017.7928129

2017

2164-3342

978-1-5090-3742-1

2017 11TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP)

3254-3258

19-24 March 2017

Paris, France

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Multi-objective optimization yields indispensable information about the best possible design trade-offs of an antenna structure, yet it is challenging if full-wave electromagnetic (EM) analysis is utilized for performance evaluation. The latter is a necessity for majority of contemporary antennas as it is the only way of achieving acceptable modeling accuracy. In this paper, a procedure for accelerated multi-objective design of antennas is proposed that exploits fast data-driven surrogates constructed at the level of coarse-discretization EM simulations, multi-objective evolutionary algorithm to yield an initial approximation of the Pareto set, and response correction methods for design refinement (i.e., elevating the selected Pareto-optimal designs to the high-fidelity EM simulation model level). To reduce the computational cost of setting up the surrogate, the relevant part of the design space (i.e., the part containing the Pareto front) is first identified through a series of single-objective optimization runs and subsequently represented by a set of adjacent compartments with separate surrogate models established within them. This segmentation process dramatically reduces the number of training samples necessary to build an accurate model thus limiting the overall optimization cost. Our approach is demonstrated using a UWB monopole antenna and compared to a state-of-the-art surrogate-assisted technique that does not use domain segmentation.

Antenna design multi-objective optimization simulation-driven design surrogate modeling design space reduction domain segmentation

其他

英语

CPCI-S ; EI

National Natural Science Foundation of China[61471258]

Engineering ; Telecommunications

Engineering, Electrical & Electronic ; Telecommunications

WOS:000403827303017

20172303737806

Approximation algorithms ; Cost reduction ; Design ; Economic and social effects ; Evolutionary algorithms ; Fuel additives ; Monopole antennas ; Pareto principle ; Ultra-wideband (UWB)

Telecommunication; Radar, Radio and Television:716 ; Radio Systems and Equipment:716.3 ; Chemical Agents and Basic Industrial Chemicals:803 ; Mathematics:921 ; Optimization Techniques:921.5 ; Social Sciences:971

Web of Science

1.Reykjavik Univ, Engn Optimizat & Modeling Ctr, IS-101 Reykjavik, Iceland
2.Gdansk Univ Technol, Fac Elect Telecommun & Informat, PL-80233 Gdansk, Poland
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
4.Univ Regina, Dept Elect Syst Engn, Regina, SK, Canada

Koziel, Slawomir,Bekasiewicz, Adrian,Cheng, Qingsha S.,et al. Accelerated Multi-Objective Design Optimization of Antennas By Surrogate Modeling and Domain Segmentation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:3254-3258.