Blending PSO and ANN for Optimal Design of FSS Filters With Koch Island Patch Elements

This work presents a new fast and accurate electromagnetic (EM) optimization technique blending the Particle Swarm Optimization (PSO) algorithm and a Multilayer Perceptrons (MLP) Artificial Neural Network (ANN). The proposed technique was applied for optimal design of Koch fractal Frequency Selective Surface (FSS) with desired stop-band filter specification. Initially, a full-wave parametric analysis was carried out for accurate EM-characterization of FSS filters. From obtained EM-dataset, a MLP network was trained with the first-order Resilient Backpropagation (RPROP) algorithm. The developed MLP model for FSS synthesis was used for efficient evaluation of cost function in PSO iterations. The advantages in the optimal design of FSS through the PSO-ANN technique were discussed in terms of convergence and computational cost. Two optimized FSS prototypes were built and measured. The accuracy of the proposed optimization technique was verified through the excellent agreement obtained by means of comparisons between theoretical and experimental results.