Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2008.2695.2702NiknamT. OlamaeiJ. AmiriB. 122008815This study presents an efficient hybrid evolutionary
optimization algorithm based on combining Ant Colony Optimization (ACO)
and Simulated Annealing (SA), called ACO-SA, for optimal clustering N
object into K clusters. The new ACO-SA algorithm is tested on several
data sets and its performance is compared with those of ACO, SA and K-means
clustering. The simulation results show that the proposed evolutionary
optimization algorithm is robust and suitable for data clustering.]]>Amir, A. and D. Lipika, 200763503527Christober C., A. Rajan and M.R. Mohan, 200729540550Fathian, M., B. Amiri and A. Maroosi,200719015021513Dorigo, M., M. Birattari and T. Stutzle,200612839Ho, S.L., Y. Shiyou, N. Guangzheng and J.M. Machado, 2006411951198Huang, S.J., 200116296301Kao, Y.T., E. Zahara and I.W. Kao,20083417541762Sim, K.M. and W.H. Sun,200333560572Lu, J.F., J.B. Tang, Z.M. Tang and J.Y. Yang, 200829787795Merkle, D., M. Middendorf and H. Schmech, 20026333346Niknam, T., A.M. Ranjbar and A.R. Shirani,200516119131Niknam, T., A.M. Ranjbar and A.R. Shirani,200529115Shelokar, P.S., V.K. Jayaraman and B.D. Kulkarni, 2004509187195Tai-Hsi, W., C. Chin-Chih and C. Shu-Hsing, 20083416091617