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.

No abstract

Biogeography-Based Optimization (BBO) is a population based algorithm which has shown impressive performance over other Evolutionary Algorithms (EAs). BBO algorithm is based on the study of distribution of biological organisms over space and time. YagiUda antenna design is most widely used antenna at VHF and UHF frequencies due to high gain, directivity and ease of construction. However, designing a Yagi-Uda antenna, that involves determination of optimal wire-lengths and their spacings, is highly complex and non-linear engineering problem. It further complicates as multiple objectives, viz. gain, and impedance, etc., are required to be optimized due to their conflicting nature, i.e., reactive antenna impedance increases significantly as antenna gain is intended to increase. In this paper Non-dominated Sorting BBO (NSBBO) is proposed and where standard and blended variants of BBO are investigated in optimizing six-element Yagi-Uda antenna designs for multiple objectives, viz., gain and impedance, where ranking of potential solutions is done using non-dominated sorting. The simulation results of BBO variants and Particle Swarm Optimization (PSO) are presented in the ending sections of the paper that depict clearly that NSBBO with blended migration operator is best option among all.

Biogeography is the study of distribution of biological species, over space and time, among random habitats. Recently developed Biogeography-Based Optimization (BBO) is a technique, where solutions of the problem under consideration are named as habitats; similar to chromosome in Genetic Algorithms (GAs) and particles in Particle Swarm Optimization (PSO). Feature sharing among various habitats, i.e., exploitation, is made to occur due to migration operator wheras exploration of new SIV values, similar to that of GAs, is accomplished with mutation operator. In this paper, various migration variants of BBO algorithm, reported till date, are investigated to optimize the lengths and spacings for YagiUda antenna elements for maximum gain. The results obtained with these migration variants are compared and the best results are presented in the ending sections of the paper.

No abstract