Abstract
Ant colony algorithm is an evolutionary optimization algorithm that simulates the foraging behavior of ant in nature, and it is distributed, parallel, robust and based on positive feedback. Basic principle of ant colony algorithm is introduced, and an adaptive clustering algorithm based on multi-ants parallel mechanism is constructed in this paper. The multi-ants parallel and adaptive clustering algorithm is applied to fault classification of locomotive wheel-paired bearings, and the accuracy rate of classification is 87%. Research results show the algorithm is effective on practical fault diagnosis.
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References
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Proceedings of the 1st European Conference on Artificial Life, pp. 134–142 (1991)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating agents. IEEE Trans. Syst., Man, Cybern. B 26, 29–41 (1996)
Prakash, S., Jayaraman, V.: An Ant Colony Approach for Clustering. Analytica Chimica Acta 509, 187–195 (2004)
Mehemet, K., Ali, N.: A New Arrhythmia Clustering Technique Base on Ant Colony Optimization. Journal of Biomedical Informatics 41, 874–881 (2008)
Rahul, K., Screeram, R.: A Hybrid Approach for Feature Subset Selection Using Neural Networks and Ant Colony Optimization. Expert Systems with Applications 33, 49–60 (2007)
Hamidreza, R., Karim, F.: An Improved Feature Selection Method Based on Ant Colony Optimization (AOC) Evaluated on Face Recognition System. Applied Mathematics and Computation 205, 716–725 (2008)
Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. Dissertation, Department of Electronics, Politecnico di Milano, Italy (1992)
Liu, H.: Principle and Application of Ant Colony Algorithm. Science Press, Beijing (2005)
Zhang, W.: Adaptive Ant Colony Optimized Clustering Algorithm Based on Multi Agent Architecture. Computer Engineering and Applications 15, 17–19 (2005)
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Zhang, Z., Cheng, W., Zhou, X. (2009). Research on Intelligent Diagnosis of Mechanical Fault Based on Ant Colony Algorithm. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_67
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DOI: https://doi.org/10.1007/978-3-642-01216-7_67
Publisher Name: Springer, Berlin, Heidelberg
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