A novel ant clustering algorithm based on cellular automata

Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.

[1]  Chris Melhuish,et al.  Stigmergy, Self-Organization, and Sorting in Collective Robotics , 1999, Artificial Life.

[2]  N. Franks,et al.  Brood sorting by ants: two phases and differential diffusion , 2004, Animal Behaviour.

[3]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[4]  Michael Batty,et al.  Cellular Automata and Urban Form: A Primer , 1997 .

[5]  Ling Chen,et al.  An adaptive ant colony clustering algorithm , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[6]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[7]  E. D. Taillard,et al.  Ant Systems , 1999 .

[8]  J. Schwartz,et al.  Theory of Self-Reproducing Automata , 1967 .

[9]  Melanie Mitchell,et al.  Evolving cellular automata to perform computations: mechanisms and impediments , 1994 .

[10]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[11]  Bastien Chopard,et al.  Cellular Automata Modeling of Physical Systems: Index , 1998 .

[12]  Richard J. Gaylord,et al.  Simulating Society: A Mathematica®Toolkit For Modeling Socioeconomic Behavior , 1998 .

[13]  Barry K. Lavine,et al.  Genetic algorithms for deciphering the complex chemosensory code of social insects , 2003 .

[14]  Yixin Chen,et al.  A novel ant clustering algorithm based on cellular automata , 2004 .

[15]  Stephen Wolfram,et al.  Universality and complexity in cellular automata , 1983 .

[16]  Eric Bonabeau,et al.  Editor's Introduction: Stigmergy , 1999, Artificial Life.

[17]  Herbert A. Simon.  Barriers and bounds to Rationality , 2000 .

[18]  Julia Handl,et al.  Improved Ant-Based Clustering and Sorting , 2002, PPSN.

[19]  D. Snyers,et al.  New results on an ant-based heuristic for highlighting the organization of large graphs , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[20]  Marco Dorigo,et al.  Mobile agents for adaptive routing , 1998, Proceedings of the Thirty-First Hawaii International Conference on System Sciences.

[21]  Juan Julián Merelo Guervós,et al.  Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning , 2004, ArXiv.

[22]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..

[23]  Peter S. Albin,et al.  Barriers and Bounds to Rationality , 1998 .

[24]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[25]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[26]  Guy Theraulaz,et al.  Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects , 1997, BCEC.

[27]  Bastien Chopard,et al.  Formation of an ant cemetery: swarm intelligence or statistical accident? , 2002, Future Gener. Comput. Syst..

[28]  G B Ermentrout,et al.  Cellular automata approaches to biological modeling. , 1993, Journal of theoretical biology.

[29]  G. Theraulaz,et al.  Inspiration for optimization from social insect behaviour , 2000, Nature.

[30]  Pascale Kuntz,et al.  A Stochastic Heuristic for Visualising Graph Clusters in a Bi-Dimensional Space Prior to Partitioning , 1999, J. Heuristics.

[31]  Baldo Faieta,et al.  Diversity and adaptation in populations of clustering ants , 1994 .