A parameter-free barebones particle swarm algorithm for unsupervised pattern classification

Article type: Research Article

Authors: Al-Sharhan, Salah^{; *} | Omran, M.G.H.

Affiliations: Computer Science Department, Gulf University for Science and Technology, Hawally, Kuwait

Correspondence: [*] Corresponding author: Salah Al-Sharhan, Computer Science Department, Gulf University for Science and Technology, P.O. Box 7207, Hawally 32093, Kuwait. E-mail: alsharhans@gust.edu.kw

Abstract: This paper introduces an efficient algorithm for unsupervised clustering that is based on barebones Particle Swarm (BB). The proposed algorithm introduces significant enhancement to the Particle Swarm Optimization (PSO) by eliminating the parameters tuning. The Algorithm aims at finding the centroids of predefined number of clusters where each centroid attracts similar patterns. This research tests and investigates the application of the proposed algorithm to the problem of unsupervised pattern classification by applying the algorithm to segmentations of different images. Experimental results show that the the proposed BB-based algorithm outperforms other state-of-the-art clustering algorithms on all the different levels of comparison. The impact of eliminating the parameters tuning is evident on the performance of the algorithm. In addition, the influence of different values for the swarm size of BB on performance is also illustrated.

Keywords: Parameter based, particle swam optimization, barebones particle swarm, clustering, unsupervised classification

DOI: 10.3233/HIS-2012-0152

Journal: International Journal of Hybrid Intelligent Systems, vol. 9, no. 3, pp. 135-143, 2012