Swarm Intelligence Clustering Algorithm Based on Attractor

Abstract. Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the clus-ter number is often too high and convergence is also slow. We put for-ward a novel structure-attractor, which actively attracts and guides the ant’s behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor (SICABA for short) greatly im-proves the convergence speed and clustering quality compared with LF and also has many notable virtues such as flexibility, decentralization compared with conventional algorithms.