Abstract
The modern heuristic particle swarm optimization technique (PSO) has received great attention in many applications and research areas over the last few years, and novel PSO‐based algorithms are continuously emerging to improve its overall performance. In this article, classical PSO schemes are modified by introducing a selection operator widely used in evolutionary algorithms such as genetic algorithms, and these novel hybrid algorithms are applied to linear array synthesis using complex weights and considering directive element patterns to weigh up the improvements achieved. Up to eight classical PSO schemes are addressed by the PSO suite tested in this work, including PSO with synchronous and asynchronous updates of the swarm along with global and local topologies for both, real‐valued and binary encoding algorithms. Furthermore, representative results comparing both classical PSO schemes and the new hybrid approaches are reported and discussed, demonstrating that the selection operator clearly increases search pressure over the swarm, speeding up convergence for certain specific schemes. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 627–632, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23248