A multiagent, multiobjective clustering algorithm

Abstract

This chapter presents MACC, a multi ant colony and multiobjective clustering algorithm that can handle distributed data, a typical necessity in scenarios involving many agents. This approach is based on independent ant colonies, each one trying to optimize one particular feature objective. The multiobjective clustering process is performed by combining the results of all colonies. Experimental evaluation shows that MACC is able to find better results than the case where colonies optimize a single objective separately. © 2009 Springer-Verlag US.