The traditional PSO is characterised by its robustness, simplicity and the small number of required tuning parameters. However, premature convergence and stagnation at a local extreme are more likely to occur for more complex model parameter spaces . To address this issue, Luu et al. have developed the CPSO. Here, the traditional PSO algorithm is used, but the diversity of the swarm is increased by resetting the "worst" particles. While the preserved "best" particles are sampling for the minimum in the vicinity of the current best position, the newly generated particles are exploring the model parameter space for a potential better minimum. Once a better minimum is found, the swarm will be assembled around the new global best value. Resetting part of the population is called "competition" and will only be initiated when a premature convergence of the optimisation is found. Here the maximum radius of the swarm δ k at iteration k determines the premature convergence and is defined as: