Over the years, Frequency Selective Surfaces (FSSs) have found frequent use as radomes and spatial filters in both commercial and military applications. Past research has concentrated on the synthesis of broadband microwave absorbers and spatial filters using multilayered dielectrics via the application of Genetic Algorithms (GAs). A comprehensive research effort has not been made to apply the GA to synthesize broadband microwave absorbers and spatial filters with embedded FSS screens, which can be termed as a computationally expensive problem.; To enhance the computational efficiency of the GA, a Micro-Genetic Algorithm (MGA) is introduced in this thesis. The MGA is applied to optimize various parameters of the composite resulting in a multilayer composite that simultaneously provides a maximum reflection or transmission of both TE and TM waves for a prescribed range of frequencies and incident angles, while automatically placing an upper bound on the total thickness of the composite. Three basic types of problem geometries have been considered to illustrate the numerical efficiency of the MGA: (i) composites with no FSS screens embedded in them; (ii) composites with a single embedded FSS screen; and (iii) two FSS screens embedded in the composite.; For the first problem, a recursive formulation for layered media is utilized to calculate the reflection and transmission coefficients of the composite. The second problem is analyzed by using an Electric Field Integral Equation (EFIE) formulation in conjunction with a Method of Moments (MoM) solution. Finally, for the third problem the Generalized Scattering Matrix (GSM) approach is used to cascade multiple FSS screens and generate reflection and transmission coefficients.; The MGA has two major advantages over the Conventional Genetic Algorithms (CGAs): (i) it works with a small population base for each generation; and (ii) it reaches near-optimal regions faster than the CGAs that work with a large population base. The general choice of population size for the CGAs can range between 100 and 10000, while the MGAs typically work with a population size between 5 and 50. Numerical experiments show that using the MGA can decrease the computational run time by 50%, even for “best-case” problems for the CGAs.
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