Novel design framework for dual-band frequency selective surfaces using multi-variant differential evolution

Novel design framework for dual-band frequency selective surfaces using multi-variant differential evolution

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A.D. Boursianis, M.S. Papadopoulou, S. Nikolaidis, P. Sarigiannidis, K. Psannis, A. Georgiadis, M.M. Tentzeris, S.K. Goudos: Novel design framework for dual-band frequency selective surfaces using multi-variant differential evolution. In: Mathematics, vol. 9, no. 19, 2021.

Περίληψη

Frequency Selective Surfaces (FSSs) have become increasingly popular during the last years due to their combined characteristics, which meet, in general, the requirements of the next-generation wireless communication networks. In this work, a cross-platform design framework for FSS structures is presented and evaluated by utilizing a recently introduced evolutionary optimization algorithm, namely, the Multi-Variant Differential Evolution (MVDE). To the best of the authors knowledge, this is the first time that the MVDE algorithm is applied to a design problem in Electromagnetics. The proposed design framework is described in detail and the utilized evolutionary algorithm is assessed in terms of its performance by applying several benchmark functions. In this context, the MVDE is comparatively evaluated against other popular evolutionary algorithms. Moreover, it is applied to the design and optimization of two different representative examples of FSS structures based on three use cases of unit cell geometry. Optimization results indicate the efficacy of the proposed framework by quantifying the performance of the designed FSS structures in terms of several system metrics. The optimized FSS structures exhibit dual-band operation and quite acceptable results in the ISM frequency bands of 2.45 GHz and 5.8 GHz. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

BibTeX (Download)

@article{Boursianis2021b,
title = {Novel design framework for dual-band frequency selective surfaces using multi-variant differential evolution},
author = { A.D. Boursianis and M.S. Papadopoulou and S. Nikolaidis and P. Sarigiannidis and K. Psannis and A. Georgiadis and M.M. Tentzeris and S.K. Goudos},
url = {https://www.researchgate.net/publication/354873810_Novel_Design_Framework_for_Dual-Band_Frequency_Selective_Surfaces_Using_Multi-Variant_Differential_Evolution},
doi = {10.3390/math9192381},
year  = {2021},
date = {2021-01-01},
journal = {Mathematics},
volume = {9},
number = {19},
abstract = {Frequency Selective Surfaces (FSSs) have become increasingly popular during the last years due to their combined characteristics, which meet, in general, the requirements of the next-generation wireless communication networks. In this work, a cross-platform design framework for FSS structures is presented and evaluated by utilizing a recently introduced evolutionary optimization algorithm, namely, the Multi-Variant Differential Evolution (MVDE). To the best of the authors knowledge, this is the first time that the MVDE algorithm is applied to a design problem in Electromagnetics. The proposed design framework is described in detail and the utilized evolutionary algorithm is assessed in terms of its performance by applying several benchmark functions. In this context, the MVDE is comparatively evaluated against other popular evolutionary algorithms. Moreover, it is applied to the design and optimization of two different representative examples of FSS structures based on three use cases of unit cell geometry. Optimization results indicate the efficacy of the proposed framework by quantifying the performance of the designed FSS structures in terms of several system metrics. The optimized FSS structures exhibit dual-band operation and quite acceptable results in the ISM frequency bands of 2.45 GHz and 5.8 GHz. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
keywords = {Design framework, Evolutionary algorithm, Frequency selective surface, Meta-heuristics, Multi-variant differential evolution, Optimization process, radio frequency energy harvesting},
pubstate = {published},
tppubtype = {article}
}
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