Particle Swarm Optimization (PSO) is one of the popular bio inspired artificial algorithm which is based on the communal behavioural aspects linked with bird gathering to resolve various optimization problems. In this research work, we propose a set of rules by formulating the mechanism for survival of the fittest, feigns the race attitude among the swarms. Based on this spect of swarms, we suggested a modified Multi-Swarm PSO (MSPSO) to solve multi-dimensional optimization problems. Further we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM). The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based methods on 4 benchmark datasets, taken from UCI machine learning.

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Particle swarm optimization feature selection data mining support vector machines