A hybrid algorithm for solving clustering problems

Abstract

There exist several approaches for solving clustering problems. Hopfield networks and self-organizing maps are the main neural approaches studied for solving clustering problems. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions and the lack of generalization of the self-organizing approaches. Genetic algorithms are the other most studied bio-inspired approaches for solving optimization problems as the clustering problems. However, the requirement of tuning many internal parameters and operators is the main disadvantage of the genetic algorithms. This paper proposes a new technique which enables feasible solutions, removes the tuning phase, and improves solutions quality of clustering problems. Moreover, several biology inspired approaches are analyzed for solving traditional benchmarks. © 2007 Springer-Verlag Berlin Heidelberg.