Title: Performance analysis of firefly algorithm for data clustering
Authors: Hema Banati; Monika Bajaj
Addresses: Department of Computer Science, University of Delhi, Delhi 110007, India ' Department of Computer Science, University of Delhi, Delhi 110007, India
Abstract: Extraction of relevant information from web is gaining immense significance. This depends upon the efficacy of methods employed to represent, organise and access the information. The most important technique that is used to organise the data is clustering. Clustering problem refers to partitioning unlabeled data objects into certain number of clusters with the objective of maximum homogeneity within cluster and heterogeneity between the clusters. The paper studies viability of firefly algorithm for clustering. It incorporates the flashing behaviour of fireflies to achieve the optimal solution. The performance of the proposed algorithm FClust is compared with particle swarm optimisation (PSO) and differential evolution (DE) algorithms with respect to varied statistical criteria using artificial and benchmark datasets. A detailed convergence behaviour of algorithms is studied using run length distribution. The experimental results prove that the proposed algorithm performs better in terms of speed and success rate as compared to PSO and DE.
Keywords: firefly algorithm; data clustering; performance evaluation; particle swarm optimisation; PSO; differential evolution; convergence behaviour; run length distribution.
International Journal of Swarm Intelligence, 2013 Vol.1 No.1, pp.19 - 35
Received: 11 May 2012
Accepted: 01 Oct 2012
Published online: 05 Jul 2014 *