A self-organizing genetic algorithm for UWB microstrip antenna optimization using a machine learning technique

Abstract

This paper presents an application of a machine learning technique to enhance a multi-objective genetic algorithm to estimate fitness function behaviors from a set of experiments made in laboratory to analyze a microstrip antenna used in ultra-wideband (UWB) wireless devices. These function behaviors are related to three objectives: bandwidth, return loss and central frequency deviation. Each objective (modeled as dependent of an antenna slit dimensions Ls and Ws) is used inside an aggregate adaptive weighted fitness function that estimates the multi-objective behavior in the algorithm. The final results were compared with the ones obtained with a similar antenna modeled in a simulator program and with the ones of a real prototype antenna built from the optimal values obtained after the optimization. The final comparison has shown a promising gain for the designed antenna in the analyzed frequencies. © 2012 Springer-Verlag.