UCLA

In this work, two nature-inspired optimization techniques, namely Particle Swarm Optimization (PSO) and Covariance Matrix Adaptation Evolution Strategy (CMAES), are presented and compared. First, comparisons of each algorithm in resource limited problems are provided using mathematical functions as well as real-world antenna design problems. In particular, a dual polarized weather radar antenna array element is optimized for use in newly proposed weather radar systems. In the last half of this work, PSO is applied to two other antenna systems. The first application investigates the use of a smoothed Sigmoid septum design in circular waveguide for possible use in high power microwave systems. PSO is also applied on two newly proposed reconfigurable E-shaped patch antenna designs, which include a polarization (RHCP/LHCP) reconfigurable and a frequency reconfigurable design. Both designs are optimized using a simple MEMS circuit model for fast optimization and measured, and possible bias network implementations are discussed.