Computational Intelligence-Based Methodology for Antenna Development

The antenna design is a challenging task, which might be time-consuming using conventional computational methods that typically require high computational capability, due to the need for several sweeps and re-running processes. This work proposes an efficient and accurate computational intelligence-based methodology for the antenna design and optimization. The computational technical solution consists of a surrogate model application, composed of a Multilayer Perceptron (MLP) artificial neural network with backpropagation for the regression process. Combined with the surrogate model, two multiobjective optimization meta-heuristic strategies, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), are used to overcome the mentioned issues from the traditional antenna design method. A study of case considering a dipole antenna for the 3.5 GHz 5G band is reported, as proof of the proposed methodology concept. Comparisons of antenna impedance matching obtained by the proposed methodology, numerical full-wave results from ANSYS HFSS and experimental result from the antenna prototype are performed for demonstrating its applicability and effectiveness for antenna development.