Particle swarm optimisation has been successfully applied to train feedforward
neural networks in static environments.Many real-world problems to which neural networks
are applied are dynamic in the sense that the underlying data distribution changes over time.
In the context of classification problems, this leads to concept drift where decision boundaries
may change over time. This article investigates the applicability of dynamic particle
swarm optimisation algorithms as neural network training algorithms under the presence of