@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}

}

@article{Boursianis2021c,

title = {Emerging swarm intelligence algorithms and their applications in antenna design: The gwo, woa, and ssa optimizers},

author = { A.D. Boursianis and M.S. Papadopoulou and M. Salucci and A. Polo and P. Sarigiannidis and K. Psannis and S. Mirjalili and S. Koulouridis and S.K. Goudos},

url = {https://www.researchgate.net/publication/354446201_Emerging_Swarm_Intelligence_Algorithms_and_Their_Applications_in_Antenna_Design_The_GWO_WOA_and_SSA_Optimizers},

doi = {10.3390/app11188330},

year  = {2021},

date = {2021-01-01},

journal = {Applied Sciences (Switzerland)},

volume = {11},

number = {18},

abstract = {Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {Antenna design, Aperture-coupled antenna, Grey wolf optimizer, Meta-heuristics, Nature-inspired algorithms, Optimization technique, Salp swarm algorithm, Swarm intelligence, Whale optimization algorithm},

pubstate = {published},

tppubtype = {article}

}