Variance Based Particle Swarm Optimization for Function Optimization and Feature Selection

Soft computing based techniques have been widely used in multi-objective optimization problems such as multi-modal function optimization, control and automation, network routing and feature selection etc. Feature Selection (FS) in high dimensional data can be modeled as multi-objective optimization problem to reduce the number of features while improving the overall accuracy. Generally, the traditional local optimization methods may not achieve this twin goal as there are many locally optimal solutions. Recently, various flavors of Particle Swarm Optimization (PSO) have been successfully applied for function optimization. The main issue in these variants of PSO is that it gets stuck in local optimum. In this paper, we have developed a novel variant of PSO which controls the velocity of particles in a swarm. We have named the proposed method as Variance Particle Swarm Optimization (VPSO) henceforth. In VPSO, the velocity is influenced by the variance of the population. When the variance of the population is high, particles make use of exploitation and vice versa. This reduces the effect of swamping in local optimum. We have validated VPSO method for function optimization and feature selection. Our proposed VPSO method achieves significantly better results against the various PSO methods on eight publicly available benchmark functions optimization and on five publicly available benchmark datasets for feature selection.