The purpose of this paper is to show how the search algorithm known as par- ticle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possi- ble applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm. Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum de- sign in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of ecient computation with very large numbers of concurrently operating processors.