×

A clustering approach using cooperative artificial bee colony algorithm. (English) Zbl 1201.68112

Summary: Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

MSC:

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C59 Approximation methods and heuristics in mathematical programming

Software:

UCI-ml; ABC
PDFBibTeX XMLCite
Full Text: DOI EuDML

References:

[1] M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, Cambridge, Mass, USA, 2004. · Zbl 1092.90066
[2] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, December 1995.
[3] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52-67, 2002. · doi:10.1109/MCS.2002.1004010
[4] D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Computer Engineering Department, Engineering Faculty, Erciyes University, 2005.
[5] D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 687-697, 2008. · Zbl 05391631 · doi:10.1016/j.asoc.2007.05.007
[6] D. Karaboga and B. Basturk, “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems,” in Proceedings of the 12th International Fuzzy Systems Association World Congress on Foundations of Fuzzy Logic and Soft Computing (IFSA ’07), vol. 4529 of Lecture Notes in Artificial Intelligence, pp. 789-798, Springer, 2007. · Zbl 1149.90186
[7] D. Karaboga and B. Akay, “Artificial bee colony (ABC) Algorithm on training artificial neural networks,” in Proceedings of the 15th IEEE Signal Processing and Communications Applications (SIU ’07), pp. 1-4, June 2007. · Zbl 1169.65053 · doi:10.1109/SIU.2007.4298679
[8] C. Ozturk and D. Karaboga, “Classification by neural networks and clustering with artificial bee colony (ABC) algorithm,” in Proceedings of the 6th International Symposium on Intelligent and Manufacturing Systems, Features, Strategies and Innovation, Sakarya, Turkey, October 2008.
[9] M. A. Potter and K. A. de Jong, “A cooperative coevolutionary approach to function optimization,” in Proceedings of the 3rd Conference on Parallel Problem Solving from Nature, pp. 249-257, Springer, Berlin, Germany, 1994.
[10] F. van den Bergh and A. P. Engelbrecht, “A cooperative approach to participle swam optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225-239, 2004. · Zbl 05452218 · doi:10.1109/TEVC.2004.826069
[11] H. Chen, Y. Zhu, and K. Hu, “Cooperative bacterial foraging optimization,” Discrete Dynamics in Nature and Society, vol. 2009, Article ID 815247, 17 pages, 2009. · Zbl 1175.90425 · doi:10.1155/2009/815247
[12] I. E. Evangelou, D. G. Hadjimitsis, A. A. Lazakidou, and C. Clayton, “Data mining and knowledge discovery in complex image data using artificial neural networks,” in Proceedings of the Workshop on Complex Reasoning an Geographical Datal, Paphos, Cyprus, 2001.
[13] M. S. Kamel and S. Z. Selim, “New algorithms for solving the fuzzy clustering problem,” Pattern Recognition, vol. 27, no. 3, pp. 421-428, 1994. · doi:10.1016/0031-3203(94)90118-X
[14] M. Omran, A. Salman, and A. P. Engelbrecht, “Image classification using particle swarm optimization,” in Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, Singapore, 2002. · doi:10.1016/0031-3203(94)90118-X
[15] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Academic Press, New York, NY, USA, 2001. · Zbl 1230.68018
[16] B. Zhang and M. Hsu, “K-harmonic means-a data clustering algorithm,” Tech. Rep. HPL-1999-124, Hewlett-Packard Labs, 2000.
[17] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981. · Zbl 0503.68069
[18] P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni, “An ant colony approach for clustering,” Analytica Chimica Acta, vol. 509, no. 2, pp. 187-195, 2004. · doi:10.1016/j.aca.2003.12.032
[19] Y. Kao and K. Cheng, “An ACO-based clustering algorithm,” in Proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS ’06), M. Dorigo, Ed., vol. 4150 of Lecture Notes in Computer Science, pp. 340-347, Springer, Berlin, Germany, 2006.
[20] M. Omran, A. P. Engelbrecht, and A. Salman, “Particle swarm optimization method for image clustering,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 19, no. 3, pp. 297-321, 2005. · Zbl 1082.68856 · doi:10.1142/S0218001405004083
[21] V. D. Merwe and A. P. Engelbrecht, “Data clustering using particle swarm optimization,” in Proceedings of IEEE Congress on Evolutionary Computation (CEC ’03), pp. 215-220, Canbella, Australia, 2003.
[22] D. Karaboga and C. Ozturk, “A novel clustering approach: artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 11, pp. 652-657, 2010.
[23] C. L. Blake and C. J. Merz, “UCI Repository of Machine Learning Databases,” http://archive.ics.uci.edu/ml/datasets.html.
[24] B. Akay and D. Karaboga, “Parameter tuning for the artificial bee colony algorithm,” in Proceeding of the 1st International Conference on Computational Collective Intelligence (ICCCI ’09), pp. 608-619, Wroclaw, Poland, 2009. · Zbl 1169.65053 · doi:10.1007/978-3-642-04441-0-53
[25] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007. · Zbl 1149.90186 · doi:10.1007/s10898-007-9149-x
[26] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC ’99), vol. 3, pp. 1945-1950, Piscataway, NJ, USA, 1999.
[27] K. R. \vZalik, “An efficient k-means clustering algorithm,” Pattern Recognition Letters, vol. 29, no. 9, pp. 1385-1391, 2008. · doi:10.1016/j.patrec.2008.02.014
[28] Z. Güngör and A. Ünler, “K-harmonic means data clustering with simulated annealing heuristic,” Applied Mathematics and Computation, vol. 184, no. 2, pp. 199-209, 2007. · Zbl 1114.65009 · doi:10.1016/j.amc.2006.05.166
[29] T. Niknam, B. Bahmani Firouzi, and M. Nayeripour, “An efficient hybrid evolutionary algorithm for cluster analysis,” World Applied Sciences Journal, vol. 4, no. 2, pp. 300-307, 2008.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.