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Computationally Efficient Multi-Objective Optimization of and Experimental Validation of Yagi-Uda Antenna

Abstract

In this paper, computationally efficient multi-objective optimization of antenna structures is discussed. As a design case, we consider a multi-parameter planar Yagi-Uda antenna structure, featuring a driven element, three directors, and a feeding structure. Direct optimization of the high-fidelity electromagnetic (EM) antenna model is prohibitive in computational terms. Instead, our design methodology exploits response surface approximation (RSA) models constructed from sampled coarse-discretization EM simulation data. The RSA model is utilized to determine the Pareto optimal set of the best possible trade-offs between conflicting objectives. In order toalleviate the difficulties related to a large number of designable parameters, the RSA model is constructed in the initially reduced design space, where the lower/upper parameter bounds are estimated by solving appropriate single-objective problems resulting in identifying the extreme point of the Pareto set. The main optimization engine is multi-objective evolutionary algorithm (MOEA). Selected designs are subsequently refined using space mapping technique to obtain the final representation of the Pareto front at the high-fidelity EM antenna model level. The total design cost corresponds to less than two hundred of EM antenna simulations.

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Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:
4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications strony 798 - 805
Language:
English
Publication year:
2014
Bibliographic description:
Bekasiewicz A., Kozieł S., Leifsson L.: Computationally Efficient Multi-Objective Optimization of and Experimental Validation of Yagi-Uda Antenna// 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications/ Vienna, Austria: , 2014, s.798-805
DOI:
Digital Object Identifier (open in new tab) 10.5220/0005136607980805
Verified by:
Gdańsk University of Technology

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