KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification

• DOI: 10.48550/arxiv.0803.2695
• Publication Date: 2008-01-01
• Authors: Carlos M. Fernandes; Antonio M. Mora; J. J. Merelo; Vitorino Ramos; Juan Luís Jiménez...
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Neural and Evolutionary Computing; 02 engineering and technology; Neural and Evolutionary Computing (cs.NE)

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