The vector evaluated particle swarm optimisation (VEPSO) algorithm is a multi-swarm variation of particle swarm optimisation (PSO) used to solve static multi-objective optimisation problems (SMOOPs). Recently, VEPSO was extended to the dynamic VEPSO (DVEPSO) algorithm to solve dynamic multi-objective optimisation problems (DMOOPs) that have at least one objective that changes over time. The search process of DVEPSO is driven through local and global guides that can be updated in various ways. This paper investigates the influence of various guide update approaches on the performance of DVEPSO. DVEPSO is also compared against a competitive-cooperative evolutionary algorithm. The results indicate that DVEPSO performs well in fast changing environments, but struggles to converge to discontinuous Pareto-optimal fronts (POFs).