Centroid particle swarm optimisation for high-dimensional data classification

ABSTRACT

This paper proposes a new variant of Particle Swarm Optimization (PSO), dubbed CentroidPSO, to tackle data classification problem in high dimensional domains. It is inspired by the center-based sampling theory, which states that the center region of a search space contains points with higher probability to be closer to the optimal solution. The experimental results show striking performance of the CentroidPSO as compared to the standard PSO, four closely related PSO variants, and three recent evolutionary computation approaches. Moreover, a comparison with three machine learning approaches indicate that the CentroidPSO is a very competitive and promising classifier.