An improved particle swarm optimization algorithm with quadratic interpolation

Abstract

In order to overcome the problems of premature convergence frequently in Particle Swarm Optimization(PSO), an improved PSO is proposed(IPSO). After the update of the particle velocity and position, two positions from set of the current personal best position are closed at random. A new position is produced by the quadratic interpolation given through three positions, i.e., global best position and two other positions. The current personal best position and the global best position are updated by comparing with the new position. Simulation experimental results of six classic benchmark functions indicate that the new algorithm greatly improves the searching efficiency and the convergence rate of PSO. © 2013 Springer-Verlag.