Metaheuristic regression equations for split-ring resonator using time-varying particle swarm optimization algorithm

MA Mughal, T Ejaz, Arshad, A Hussain - Electronics, 2018 - mdpi.com
Electronics, 2018mdpi.com
This article presents a new technique for determining accurate values of resonant frequency
and quality factor pertaining to the split-ring resonator. Different conducting shield materials
have been used around a copper split-ring. The split-ring has been designed to operate at
about 2.1 GHz. Various equations were worked out earlier to determine the values of
resonant frequency and quality factor. However, these equations yielded different solutions.
Therefore, simulations were used to obtain the values of the resonant frequency and quality …
This article presents a new technique for determining accurate values of resonant frequency and quality factor pertaining to the split-ring resonator. Different conducting shield materials have been used around a copper split-ring. The split-ring has been designed to operate at about 2.1 GHz. Various equations were worked out earlier to determine the values of resonant frequency and quality factor. However, these equations yielded different solutions. Therefore, simulations were used to obtain the values of the resonant frequency and quality factor of the split-ring resonator with different five-shield materials, using High-Frequency Structure Simulator (HFSS) software. In this work, a novel method has been introduced for obtaining values of resonant frequency which provides results with negligible error. An optimal technique, namely time-varying particle swarm optimization (TVPSO), was then performed to obtain two sets of equations for resonant frequency and quality factor. The two sets of equations, optimized using TVPSO, were compared for their effectiveness in matching the actual frequency and quality factor for each of the five materials. It was found that the TVPSO was significant in achieving the frequency and quality factor regression equation to accurately resemble the actual values portrayed by the low mean absolute error.
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