Abstract
Various clustering methods based on the behaviour of real ants have been proposed. In this paper, we develop a new algorithm in which the behaviour of the artificial ants is governed by fuzzy IF–THEN rules. Our algorithm is conceptually simple, robust and easy to use due to observed dataset independence of the parameter values involved.
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Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California(1998), available at http://www.ics.uci.edu/mlearn/MLRepository.html
Bonabeau, E., Sobkowski, A., Theraulaz, G., Deneubourg, J.L.: Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects. Working Paper 98-01-004 (1998), available at http://ideas.repec.org/p/wop/safiwp/98-01-004.html
Deneubourg, J.L., Goss, S., Franks, N., Sendova–Franks, A., Detrain, C., Chrétien, L.: The Dynamics of Collective Sorting Robot–Like Ants and Ant–Like Robots. In: From Animals to Animats: Proc. of the 1st Int. Conf. on Simulation of Adaptive Behaviour, pp. 356–363 (1990)
Handl, J., Meyer, B.: Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface. In: Proc. of the 7th Int. Conf. on Parallel Problem Solving from Nature, pp. 913–923 (2002)
Hölldobler, B., Wilson, E.O.: The ants. Springer, Heidelberg (1990)
Kanade, P.M., Hall, L.O.: Fuzzy Ants as a Clustering Concept. In: Proc. of the 22nd Int. Conf. of the North American Fuzzy Information Processing Soc., pp. 227–232 (2003)
Luc̆ić, P.: Modelling Transportation Systems using Concepts of Swarm Intelligence and Soft Computing. PhD thesis, Virginia Tech. (2002)
Lumer, E.D., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: From Animals to Animats 3: Proc. of the 3th Int. Conf. on the Simulation of Adaptive Behaviour, pp. 501–508 (1994)
Mamdani, E.H., Assilian, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. Int. J. of Man-Machine Studies 7, 1–13 (1975)
Monmarché, N.: Algorithmes de Fourmis Artificielles: Applications à la Classification et à l’Optimisation, PhD thesis, Université François Rabelais (2000)
Klement, E.P., Mesiar, R., Pap, E.: Triangular norms. Kluwer Academic Publishers, Dordrecht (2002)
Ramos, V., Muge, F., Pina, P.: Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies. Soft Computing Systems: Design, Management and Applications, 500–509 (2002)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
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Schockaert, S., De Cock, M., Cornelis, C., Kerre, E.E. (2004). Fuzzy Ant Based Clustering. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2004. Lecture Notes in Computer Science, vol 3172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_33
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DOI: https://doi.org/10.1007/978-3-540-28646-2_33
Publisher Name: Springer, Berlin, Heidelberg
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