Many optimisation problems have more than one objective that are in conflict with one another and that change over time, called dynamic multi-objective problems. To solve these problems an algorithm must be able to track the changing Pareto Optimal Front (POF) over time and find a diverse set of solutions. This requires detecting that a change has occurred in the environment and then responding to the change. Responding to the change also requires to update the archive of non-dominated solutions that represents the found POF. This paper discusses various ways to manage the archive solutions when a change occurs in the environment. Furthermore, two new benchmark functions are presented where the POF is discontinuous. The dynamic Vector Evaluation Particle Swarm Optimisation (DVEPSO) algorithm is tested against a variety of benchmark function types and its performance is compared against three state-of-the-art DMOO algorithms.