Transition from particle swarm optimization to individual particle optimization

Abstract

This paper proposes a novel optimization method in order to real time implementation based on standard particle swarm optimization idea and characteristics of chaotic maps which is named as individual particle optimization (IPO). Three typical benchmark functions are used to validate the proposed algorithm performance and runtime and then compare with that of the other algorithms known as modified PSOs. Six functions reported in SIS2005 are used to better verification of the proposed algorithm. Numerical results indicate that IPO is competitive due to its more competitive convergence rate and ability to find the functions' global optimum.