Self-Organizing Swarm (SOSwarm): A Particle Swarm algorithm for unsupervised learning

We present a novel self-organizing Particle Swarm algorithm, SOSwarm, that adopts unsupervised learning. Input vectors are projected onto a lower dimensional map space producing a visual representation of the input data in a manner similar to the Self-Organizing Map (SOM) artificial neural network. Particles in the map react to the input data by modifying their velocities using a standard Particle Swarm Optimization update function, and therefore organize themselves spatially within fixed neighborhoods in response to the input training vectors. SOSwarm is successfully applied to four benchmark classification problems from the UCI Machine Learning repository with the novel SOSwarm algorithm outperforming or equaling the best reported results on all four of the problems analyzed.