Multi-guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces.

This article presents an investigation into the effects of the search space dimension on the control parameter importance of the multi-guide particle swarm optimization (MGPSO) algorithm over time. The MGPSO algorithm is a multi-objective optimization algorithm that uses multiple swarms, each swarm focusing on an individual objective. This relative control parameter importance of the MGPSO is identified using functional analysis of variance (fANOVA). The fANOVA process quantifies the control parameter importance through analysing variance in the objective function values associated with a change in control parameter values. The results indicate that the inertia component value is the most influential control parameter to tune when optimizing the MGPSO throughout the run time. The relative importance of the inertia weight remains dominant with an increase in the search space dimensions.