Dynamic Data Clustering Using Stochastic Approximation Driven Multi-Dimensional Particle Swarm Optimization
With an ever-growing attention Particle Swarm Optimization (PSO) has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. For this purpose we use simultaneous perturbation stochastic approximation (SPSA), which is applied only to the gbest (not to the entire swarm) for a low-cost solution. Since the problem of poor gbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), two distinct SA approaches are then integrated into MD-PSO and tested over a set of unsupervised data clustering applications. Experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.
KeywordsParticle Swarm Optimization stochastic approximation multi-dimensional search gradient descent dynamic data clustering
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