Combinational of surrogate modeling and particle swarm optimization for improving the electromagnetic performances of a frequency selective surface

Frequency-selective surfaces (FSSs) consist of the repetition of unit cells for controlling reflection, transmission/absorption of electromagnetic (EM) fields. They are typically employed at radio and optical frequencies. Simulation of such large (in terms of wavelength) structures based on the traditional EM simulations is time-consuming and requires significant computational resources. Hence, this paper devotes to present an optimization-oriented methodology for designing and optimizing FSS in an automated fashion. The FSS structure is optimized using the artificial neural network paradigm, where the particle swarm optimization is applied for sizing the design parameters. The optimization process is an automatic one where electronic design automation tool with numerical analyser is working together, leading to effectively optimize the FSS design. To verify the effectiveness of the proposed method, an FSS structure exhibiting a wide transmission band for normal incidence in the 7.0-11.2 GHz range is considered