A New Approach for FSS Design in 3.5GHz Based on General Neural Network Model by using Multi-objective Sailfish Optimization Algorithm
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Abstract
This work approaches a bioinspired hybrid multiobjective optimization technique associated with a general regression neural network as a proposal to synthesize the geometry and the dimensions of a frequency selective surface (FSS) for electromagnetic wave filtering in 5G applications. This new hybrid technique associates the bio-inspired algorithm known as the Sailfish Optimizer (SFO), together with a GRNN net to obtain the parameters for constructing the filter.In this study, the focus is on the application of the technique as a tool for the design and the synthesis of FSS, which has the shape of a square spiral unitary cell, printed on a fiberglass substrate plate (FR4). The objectives of the optimization process are to set the resonant frequency of the FSS to 3.5 GHz and the perating bandwidth to 0.8 GHz. It is reported a good agreement between the simulated and measured results.
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