Influence of Social Coefficient on Swarm Motion

Abstract

This paper presents an improved effective particle swarm optimization algorithm named SCPSO. In SCPSO, in order to overcome disadvantages connected with premature convergence, a new approach associated with the social coefficient is included. Instead of random selected social coefficients, the author has proposed dynamically changing coefficients affected by experience of particles. The presented method was tested on a set of benchmark functions and the results were compared with those obtained through MPSO-TVAC, standard PSO (SPSO) and DPSO. The simulation results indicate that SCPSO is an effective optimization method.