Most optimisation algorithms from the computational intelligence field assume that the search landscape is static. However, this assumption is not valid for many real-world problems. Therefore, there is a need for efficient optimisation algorithms that can track changing optima. A number of variants of particle swarm optimisation (PSO) have been developed for dynamic environments. Recently, the cooperative PSO has been shown to significantly improve performance of PSO in static environments, especially for high-dimensional problems. This paper investigates the performance of a cooperative version of the charged PSO on a benchmark of dynamic optimisation problems. Empirical results show that the cooperative charged PSO is an excellent alternative to track dynamically changing optima.