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.

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