Contribución a los métodos de optimización basados en procesos naturales y su aplicación a la medida de antenas en campo próximo

Abstract

For the last decade, heuristic optimization methods based on imitating natural, biological, social or cultural processes in a computational way have aroused great interest among the scientific community, due to its ability to explore efficiently multimodal and high-dimension solution spaces. On this basis, this thesis tackles the development, analysis and tuning of different traditional and heuristic optimization methods. In short, a local search method based on simplex and several heuristic methods, such as simulated annealing, genetic algorithms and particle swarm optimization are considered. For these last two algorithms different schemes are investigated so as to overcome the typical limitations of classical schemes. The tuning of the optimization methods is carried out considering as a reference problem the antenna radiation characterization from near-field measurements over a planar geometry, using a near-field to far-field transformation method based on equivalent currents. A parametric analysis is included for each optimization method, in those cases in which it has been considered necessary, as well as theoretical field transformation results obtained with aperture and pyramidal horn antennas. Results of a comparative study, carried out using theoretical sources and measurements, demonstrate the usefulness of the method and make it possible to conclude that the particle swarm optimization is the algorithm that provides the best performance for this application. The optimization methods developed and investigated in this work have also been applied to other problems, such as the synthesis of linear arrays or the source modelling in electromagnetic compatibility applications.