Optimization of FSS with Sierpinski island fractal elements using population-based search algorithms and MLP neural network

Abstract

This work presents an electromagnetic optimization technique that blending full-wave method, artificial neural network, and population-based search algorithms for optimal design of frequency selective surfaces (FSSs) with fractal motifs. We consider a simple application of this technique in a single-layer FSS with Sierpinski island fractal patch elements. The optimization technique replaces the computational intensive full-wave method of moments simulations by a fast and accurate multilayer perceptrons neural network model of FSS spatial filter, which is used to compute the cost (or fitness) function in the search algorithms iterations. In the FSS optimization with specific resonant frequency and bandwidth, we use: bees algorithm, continuous genetic algorithm, and particle swarm optimization. A FSS prototype is built and measured for a first level of Sierpinski island fractal. The accuracy of the proposed optimization technique is verified through the excellent agreement (0.9%) obtained by means of comparisons between theoretical and experimental results. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:827–831, 2014