An analysis of control parameter importance in the particle swarm optimization algorithm

Abstract

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, and appropriate tuning of these control parameters can greatly improve performance. This paper employs function analysis of variance (fANOVA) to quantify the importance of each of the three conventional PSO control parameters, namely the inertia weight (ω), the cognitive acceleration coefficient (c1), and the social acceleration coefficient (c2), according to their respective variances associated with the fitness. Results indicate that the inertia value, ω, has the greatest sensitivity to its assigned value and thus is the most important parameter to tune when optimizing PSO performance for low dimensional problems.