Using natural optimization algorithms and artificial neural networks in the design of effective permittivity of metamaterials

Metamaterials are a broad class of artificial materials that could be engineered to wield effective permittivity and permeability characteristics to system requirements. In this work, a hybrid EM-optimization method using continuous-GA blended with MLP-ANN models is used for fast and accurate evaluation of cost function into continuous-GA simulations, in order to overcome the computational requirements associated with full wave numerical simulations for an optimization of the effective permittivity of the metamaterial.