Hybrid particle swarm-based algorithms and their application to linear array synthesis

Abstract

Aheuristic particle swarm optimization (PSO) based algorithm is presented in this work and the novel hybrid approach is applied to linear array synthesis considering complex weights and directive element patterns so as to analyze its usefulness and limitations. Basically, classical PSO schemes are modified by introducing a tournament selection strategy and the downhill simplex local search method, so that the hybrid algorithms proposed combine the strengths of the PSO to initially explore the search space, the pressure exerted by the genetic selection operator to manage and speed up the search, and finally, the ability of the local optimization technique to quickly descend to the optimum solution. Four classical real-valued PSO schemes are taken as reference and synthesis results for a 60-element linear array comparing those classical schemes and the hybridized ones are reported and discussed in order to show the improvements achieved by the hybrid approaches.