The analysis of electromagnetic devices via computer software usually demands
high computational cost and high processing time. In certain situations, to
meet certain design objectives, finding the optimal structural parameters can take
days or even weeks when done by trial and error when seeking accurate answers
in highly complex structures. In this scenario, bioinspired computation (BIC) tools
are strong allies in saving time, computational cost and, consequently, money. To
enhance the power and efficiency of these tools, hybrid methods have been developed
in which neural networks work in conjunction with optimization algorithms
to obtain even more satisfactory and accurate results. In this context, this work
presents the use of two multiobjective bioinspired hybrid optimization models for
the design and synthesis of multilayer frequency selective surfaces (FSS). Initially,
an electromagnetic investigation of the unit cell of the patch-like structures that
will compose the multilayer FSS is made, which are a triangular loop and a solid
diamond printed on fiberglass substrate (FR-4). The computer simulations were
performed with the aid of CSTR○ Micro Wave Studio software, whose finite integrals
(FIT) numerical technique is used. Three filters with distinctive characteristics that
cover the C, X and Ku bands are designed. The synthesis process consists of tuning
the objectives of the structures inserted in the cost function of the optimization
algorithms. The modeling of the structures is performed by a general regression neural
network (GRNN) and the optimization process is performed by the algorithms.
The computational simulations for calculating the electromagnetic (EM) data of
the multilayer FSS were performed using the CSTR○ software. The optimized values
returned by the hybrid models were also simulated using Ansoft 𝐷𝑒𝑠𝑖𝑔𝑛𝑒𝑟𝑇𝑀 HFSS
software to evaluate the previously obtained results. Good agreement between the
simulated results was observed, showing a reduction in the processing time of the
structures, besides showing that the GRNN-AG Multi model stood out in relation to
the GRNN-MOCS, presenting errors in relation to the design objectives for the
simulations. in CSTR○ of 0.44%, 0.254% and 0.387% for filters 1, 2 and 3, respectively,
which is the most efficient hybrid model for multi-layer FSS optimization.
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