A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters

Si He (), Nabil Belacel, Alan Chan (), Habib Hamam () and Yassine Bouslimani ()
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Si He: Electrical Engineering Department, University of Moncton, Moncton, N.B., Canada
Nabil Belacel: Information and Communications Technologies, National Research Council, Moncton, N.B., Canada
Habib Hamam: Electrical Engineering Department, University of Moncton, Moncton, N.B., Canada
Yassine Bouslimani: Electrical Engineering Department, University of Moncton, N.B., Canada

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 05, 949-974

Abstract: This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experimental results show that our newly proposed approach outperforms some other well-known existing fuzzy clustering algorithms in terms of both accuracy and robustness.

Keywords: Unsupervised classification; fuzzy clustering; artificial fish swarm; simulated annealing (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S0219622016500267

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