Quantity and Quality Aware Artificial Bee Colony Algorithm for Clustering

Authors: U. Idachaba, F. Z. Wang, A. Qi, N. Helian

Abstract:

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence technique for clustering. It produces higher quality clusters compared to other population-based algorithms but with poor energy efficiency, cluster quality consistency and typically slower in convergence speed. Inspired by energy saving foraging behavior of natural honey bees this paper presents a Quality and Quantity Aware Artificial Bee Colony (Q2ABC) algorithm to improve quality of cluster identification, energy efficiency and convergence speed of the original ABC. To evaluate the performance of Q2ABC algorithm, experiments were conducted on a suite of ten benchmark UCI datasets. The results demonstrate Q2ABC outperformed ABC and K-means algorithm in the quality of clusters delivered.

Keywords: Artificial bee colony algorithm, clustering.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087055

References:

[1] E. Bonabeau, and C. Meyer, "Swarm intelligence," Harvard Business Review, vol. 79, no. 5, pp. 106-114, 2001.
[2] D. T. Pham, S. Otri, A. Afify,M. Mahmuddin, and H. Al-Jabbouli, "Data Clustering Using the Bee Algorithm," in . In Proc. 40th CIRP International Manufacturing Systems Seminar, 2007.
[3] A. Jain, M.N. Murty, and P.J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, no. 3, pp. 264-323, 1999.
[4] D. Karaboga, and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied Soft Computing, vol. 11, no. 1, pp. 652-657, 2011.
[5] W. Gao, and S. Liu, "Improved artificial bee colony algorithm for global optimization," Information Processing Letters, vol. 111, pp. 871-882, 2011.
[6] A. Frank, and A. Asuncion, "UCI Machine Learning Repository," University of California, California, 2010.