An adaptive particle swarm optimization algorithm based on optimal parameter regions
Kyle Robert Harrison, Andries Petrus Engelbrecht, Beatrice M. Ombuki-Berman · 2017
The performance of the particle swarm optimization (PSO) algorithm is known to be sensitive to the values of its control parameters. Parameter tuning is thus an important aspect in optimizing PSO performance. While many studies have examined a variety of PSO parameters and have provided general-purpose parameter suggestions, a recent study has shown that the best parameters to employ are, in fact, time-dependent. Furthermore, a priori parameter tuning is a time-consuming procedure and assumes that the best parameters to employ do not change over time. This study proposes a new PSO variant which randomly samples its control parameter values from a region known to contain promising parameter configurations, thereby eliminating the need to specify (and tune) values for the traditional PSO control parameters. The new PSO variant is compared to PSO employing 14 different parametrizations suggested in the literature. Results indicate that the performance of the proposed variant is on par with the best parameter configurations suggested in the literature.