Empirical study of particle swarm optimization

TLDR

The study empirically evaluates the performance of the particle swarm optimizer (PSO). The authors test PSO on four benchmark functions with asymmetric initial ranges. PSO converges rapidly but slows near optima, yet remains promising, and an adaptive inertia weight is proposed to improve performance near minima.

Abstract

We empirically study the performance of the particle swarm optimizer (PSO). Four different benchmark functions with asymmetric initial range settings are selected as testing functions. The experimental results illustrate the advantages and disadvantages of the PSO. Under all the testing cases, the PSO always converges very quickly towards the optimal positions but may slow its convergence speed when it is near a minimum. Nevertheless, the experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.