Abstract—This paper studies the applicability of
hybridization of Differential Evolution (DE) and PSO
techniques to data clustering problem. A new way of integrating
DE and PSO is explored in the paper. In one approach, a
parallel DE and PSO developed and in other, a transitional
approach of alternate DE and PSO technique followed.
Simulations for number of data sets show that the proposed
integrated approach provides better clustering performance.
Index Terms—Clustering, PSO, differential evolution.
The authors are with the Department of CSE, ANITS, Visakhapatnam, AP, India(e-mail: firstname.lastname@example.org; email@example.com).
Cite:Suresh Satapathy, Divya Maheshwari, Sai Hanuman A, Vinaya Babu A, P. K. Patra, and B. N. Biswal, "Integrated PSO and DE for Data Clustering," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 839-843, 2012.