Particle Swarm Optimization (PSO) is a population-based optimization method in which search points employ a cooperative strategy to move toward one another. In this paper we show that PSO appears to work well on "single-funnel" optimization functions. On more complex optimization problems, PSO tends to converge too quickly and then fail to make further progress. We contend that most benchmarks for PSO have classically been demonstrated on single-funnel functions. However, in practice, optimization tasks are more complex and possess higher problem dimensionality. We present empirical results that support our conjecture that PSO performs well on single-funnel functions but tends to stagnate on more complicated landscapes.