An Ant-Based Self-Organizing Feature Maps Algorithm

WSOM 2005, Paris5 Conclusion and Future WorkIn summary, this research has proposed an new algorithm named the ABSOM, that combinesthe ant colony system with the Kohonen’s SOM, to overcome the local minimal issue on theKohonen’s self-organizing maps. In this research, We compared the ABSOM with the conventionalSOM using four data sets with the given solutions and finally we found out the ABSOM is superiorto the conventional SOM in general.As pointed by recent researches, the fixed structure of the conventional SOMs can not afford torespond the real data distribution if the original data set consists of vague boudaries of differentclusters. Therefore, our research work will extend the proposed ABSOM approach to create agrowable ant-based SOM for solving this kind of problem.References[1] E. Bonabeau, M. Dorigo and G. Théraulaz(1999a), From natural to artificial swarmintelligence, Oxford University Press.[2] E. Bonabeau, M. Dorigo, and G. Theraulaz(1999b), Swarm Intelligence: From Natural toArtificial Systems, Oxford University Press, New York.[3] P. S. Chelokar, V. K. Jayaraman, B. D. Kulkarni(2003), An Ant Colony Approach forClustering, Analytica Chimica Acta 509(2004), p. 187-195.[4] A. Colorni, M. Dorigo, and V. Maniezzo(1991), Distributed Optimization by Ant Colonies,Proceedings of ECAL91-European Conference on Artificial Life, Paris, France, F. Varela andP. Bourgine(Eds), Elsevier Publishing, p. 134-142.[5] A. Colorni, M. Dorigo, and V. Maniezzo(1992), An Investigation of Some Properties of an AntAlgorithm, Proceedings of the Parallel Problem Solving from Nature Conference(PPSN 92).[6] M. Dorigo(1992), Optimization, Learning and Natural Algorithms, Ph.D Thesis, Dipartimentodi Elettronica e Informazione, Politecnico di Milano, Italia.[7] M. Dorigo, L. M. Gambardella(1997), Ant Colony System: A Cooperative Learning Approachto the Traveling Salesman Problem, IEEE transactions on Evolutionary Computation, vol.1,No.1.[8] M. Dorigo, V. Maniezzo and A. Colorni(1996), The Ant System:Optimization by a Colony ofCooperaing Agents, IEEE Transactions on Systems.[9] J. A, Kangas, T. Kohonen and J. T. Laaksonen(1990), Variants of Self-Organizing Maps, IEEETransactions on Neural Networks, vol. 1, No. 1, March.[10] S. Kasko, J. Kangas, T. Kohonen(1998), Bibliography of Self-Grganizing Map (SOM) Papers:1981-1997, Neural Computing Survey 1, p. 102-350.[11] K. Kiviluoto(1996), Topology Preservation in Self-Organizing Maps, In the Proceedings ofInternational Conference on Neural Networks (ICNN), p. 294-299.[12] T. Kohonen(1995), Self-Organizing Map, 2nd ed., Springer-Verlag, Berlin, p. 113.[13] T. Kohonen(1998), The Self-Organizing Map, Neurocomputing, vol 21, p. 1-6,[14] T. Kohonen(2000), Self-Organizing Maps, Springer-Verlag.[15] T. Kohonen, Self-Organizing Maps, Springer-Verlag , Berlin, Germany, 1997[16] P. Mangiameli, S. K. Chen, D. West(1996), A Comparison of SOM Neural Network andHierarchical Clustering Methods, European Journal of Operation Research, vol. 93, p. 402-417.[17] V. Maniezzo, A. Colorni, and M. Dorigo(1994), The Ant System Applied to the QuadraticAssignment Problem, Tech. Rep. IRIDIA/94-28, Universite Libre de Bruxelles, Belgium.[18] R. S. Parpinelli, H. S. Lpoes, and A. A. Freitas(2002), Data Mining With an Ant ColonyOptimization Algorithm, IEEE Transactions on Evolutionary Computing, vol 6, No. 4,August.