Particle swarm optimization (PSO) is a stochastic search algorithm based on the social dynamics of a flock of birds. The performance of the PSO algorithm is known to be sensitive to the values assigned to its control parameters. While many studies have provided reasonable ranges in which to initialize the parameters based on their long-term behaviours, such previous studies fail to quantify the empirical performance of parameter configurations across a wide variety of benchmark problems. This paper specifically address this issue by examining the performance of a set of 1012 parameter configurations of the PSO algorithm over a set of 22 benchmark problems using both the global-best and local-best topologies. Results indicate that, in general, parameter configurations which are within close proximity to the boundaries of the best-known theoretically-defined convergent region lead to better performance than configurations which are further away. Moreover, results indicate that neighbourhood topology plays a far more significant role than modality and separability when determining the regions in parameter space which perform well.