An Adaptive Evolutionary Algorithm for UWB Microstrip Antennas Optimization Using a Machine Learning Technique

ABSTRACT This article presents an application of a machine learning technique to enhance a multiobjective evolutionary algorithm to estimate fitness function behaviors from a set of experiments made in laboratory to analyze a microstrip antenna used in ultra wideband wireless devices. These function behaviors are related to three objectives: bandwidth, return loss, and central frequency deviation. Each objective is used inside an aggregate adaptive weighted fitness function that estimates the behavior in the algorithm. The machine learning technique enabled a dynamic estimation of an aggregated compound fitness function and made it possible to a prototype algorithm to learn and adapt with a set of experiments stored in a web system repository. The final results were then 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. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1864–1868, 2013