Convergence of a particle swarm optimization algorithm

In recent years particle swarm optimization (PSO) has been successfully applied to many research and engineering optimization problems. PSO has shown superior performance over other global optimization algorithms for problems with a large number of dimensions, or when the response surface is highly multimodal. We investigate the effects of dimensionality and internal PSO parameters on the rate of convergence.