KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern   Classification
  In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems. 
C. Fernandes, A.M. Mora, J.J. Merelo, V. Ramos, J.L.J. Laredo