Abstract
Recently, many kinds of particle swarm optimization (PSO) algorithms have been proposed to improve the performance of the standard one. However, the systematic analysis of the behavior of such algorithms is not sufficient. This paper extends the previous analysis results for the standard PSO algorithm to two types of PSO algorithms. The one is a PSO algorithm with multiswarms, and its properties of decay rate and l2 gain are analyzed. The decay rate and l2 gain are closely related to the convergence speed and swarm diversity,respectively, of the algorithm, which also corresponds to exploitation and exploration abilities. The other one is the type of PSO algorithm with time-varying parameters. The characteristics of the parameter settings are investigated from the viewpoints of the convergence speed and swarm diversity.
