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.
The paper presents a comparative analysis of data clustering by Particle swarm optimization (PSO) and differential evolution (DE) techniques. It is clearly reveled from the simulation results that almost parameter free optimization technique such as Differential evolution could provide better performance compared to PSO where in many parameters are to be tuned. To exhibit the numerical optimizing capability of DE we have demonstrated the capability of this by optimizing few benchmark functions.
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