Cooperative Charged Particle Swarm Optimiser

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 [1] 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.