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FSS designs using a population-based hybrid algorithm inspired on the echolocation of bats

A hybrid optimization technique based on natural computing is presented in this work. Particularly, a population-based hybrid algorithm inspired on the echolocation of bats is applied to design frequency selective surfaces (FSS). To support the search, an artificial neural network (ANN) is used. The algorithm aims to optimize the design of FSS, through the bandwidth and frequency search. The bat algorithm (BAT) finds, in the performed search, optimized values for the FSS main parameters from variations of the dipole width (W) of its patch elements. This analysis results were compared to those provided by the wave concept iterative procedure (WCIP) analytical method and measurements and agreement is observed.