Evolutionary performance of BBO and PSO algorithms for Yagi-Uda antenna design optimization

Particle Swarm Optimization (PSO) and Biogeography Based Optimization (BBO) are most popular swarm based optimization algorithms those have shown impressive performance over other Evolutionary Algorithms (EAs). Yagi-Uda is one of most widely antenna designs used at High Frequency (HF) and Ultra High Frequency (UHF) due its high gain, low cost and constructional ease. Designing a Yagi-Uda antenna involves determination of wire-element lengths and their spacings in between them those bear highly complex and non-linear relationships with antenna gain, impedance and Single Lobe Level (SLL) at a particular frequency of operation. In this paper, a comparative study between PSO and BBO is presented for faster optimization of antenna designs for maximum gain. The best antenna designs are tabulated and average of 10 monte-carlo simulations are plotted for BBO, PSO and their combinational iterative performances in the ending sections.