Position update mechanisms for enhanced particle swarm classification

This work addresses position update mechanisms that may increase the accuracy of particle swarm classification (PSC), a derivative of Particle Swarm Optimization (PSO) fit for classification problems. The main idea in PSC is to retrieve the best particle positions corresponding to the centroids of classes. We present two variants of the PSC algorithm with different position update mechanisms. In particular, we show how the combination of particle confinement to the search space and a biologically inspired wind dispersion mechanism for them improves the classification accuracy of the basic PSC algorithm. An experimental set up was realized and tested on five benchmark databases, leading to better recognition accuracies than those obtained with the previous PSC algorithm.