2022
Maria S Papadopoulou; Achilles D Boursianis; Argyrios Chatzopoulos; Panagiotis Sarigiannidis; Spyridon Nikolaidis; Sotirios K Goudos
Comparative Performance of Algorithmic Techniques for Optimizing Dual-Band Rectifier Conference
2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2022, ISBN: 978-1-6654-6717-9.
Abstract | BibTeX | Tags: genetic algorithm, impedance matching, Optimization technique, rectenna, rectifier, RF energy harvesting, wireless sensor network | Links:
@conference{9837645,
title = {Comparative Performance of Algorithmic Techniques for Optimizing Dual-Band Rectifier},
author = {Maria S Papadopoulou and Achilles D Boursianis and Argyrios Chatzopoulos and Panagiotis Sarigiannidis and Spyridon Nikolaidis and Sotirios K Goudos},
url = {https://www.researchgate.net/publication/362330125_Comparative_Performance_of_Algorithmic_Techniques_for_Optimizing_Dual-Band_Rectifier},
doi = {10.1109/MOCAST54814.2022.9837645},
isbn = {978-1-6654-6717-9},
year = {2022},
date = {2022-06-08},
booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},
pages = {1-4},
abstract = {Radio Frequency (RF) energy harvesting (EH) is a technique to replenish the source of wireless sensor networks (WSNs). Also, many interdisciplinary fields in the Internet-of-Things (IoT) era use RF-EH, like precision agriculture, biomedical, and robotics. Over the years, various designs have been presented in the literature operating in multi- or wide-band frequencies. Usually, a designed system is optimized using specific goals and optimization parameters to obtain maximization in power conversion efficiency (PCE). In this work, a dual-band RF rectifier system that resonates in the Wi-Fi frequency bands of 2.45 GHz and 5.8 GHz is presented. The proposed system is optimized using four optimization techniques, namely the Gradient algorithm, the Minimax algorithm, the Simulated Annealing, and the Genetic algorithm. A set of comparative results is presented to assess the performance of each technique and to obtain the feasible solution of the proposed design. Numerical results demonstrate that a 42.8% efficiency is achieved, having a 16 dBm input power and a 1.7 kΩ output resistance load.},
keywords = {genetic algorithm, impedance matching, Optimization technique, rectenna, rectifier, RF energy harvesting, wireless sensor network},
pubstate = {published},
tppubtype = {conference}
}
2021
A.D. Boursianis; M.S. Papadopoulou; M. Salucci; A. Polo; P. Sarigiannidis; K. Psannis; S. Mirjalili; S. Koulouridis; S.K. Goudos
Emerging swarm intelligence algorithms and their applications in antenna design: The gwo, woa, and ssa optimizers Journal Article
In: Applied Sciences (Switzerland), vol. 11, no. 18, 2021.
Abstract | BibTeX | Tags: Antenna design, Aperture-coupled antenna, Grey wolf optimizer, Meta-heuristics, Nature-inspired algorithms, Optimization technique, Salp swarm algorithm, Swarm intelligence, Whale optimization algorithm | Links:
@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}
}
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