Design of Yagi-Uda antennas using comprehensive learning particle swarm optimisation

A method of using particle swarm optimisation (PSO) algorithms to optimise the element spacing and lengths of Yagi-Uda antennas is presented. SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2) is used to evaluate the performance of various Yagi-Uda antenna designs. In order to show the capabilities of the PSO algorithm in Yagi-Uda antenna design, three different antenna design cases are optimised for various performance specifications. The three objectives considered are gain only, gain and input impedance only, and gain, input impedance and relative sidelobe level (rSLL). To alleviate the premature convergence problem of PSO, a novel learning strategy is employed. Each design problem is optimised using three variants of PSO algorithms, namely the modified PSO, fitness-distance ratio PSO (FDR-PSO), and comprehensive learning PSO (CLPSO). For the purpose of comparison and benchmarking, equally spaced arrays, genetic algorithm optimised antenna design, and computational intelligence optimised antenna design are considered. The results clearly show that the CLPSO is a robust and useful optimisation tool for designing Yagi antennas for the desired target specifications.