Hybrid and Bioinspired Computational Optimization Techniques for the Design and Synthesis of Multilayer FSS

Abstract

This article presents two hybrid and bioinspired optimization techniques that associate the General Regression Neural Network (GRNN) to a Genetic Algorithm (GRNN-GA) and to a Cuckoo Search (GRNN-CS) algorithm, developed for the synthesis and designing process of two multilayered Frequency Selective Surfaces (FSS) that operate at the X and Ku frequency bands. The cutoff frequencies, that signal how much bandwidth the surfaces provide, are obtained at -10dB. Data calculated for both techniques, GRNN-GA and GRNN-CS, are herein compared, and both have shown to produce satisfactory results.