A Simpler and More Effective Particle Swarm Optimization Algorithm

Abstract

The basic particle swarm optimization (bPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. Three algorithms, based on the simple evolutionary equations and the extrenum disturbed arithmetic operators, are proposed to overcome the demerits of the bPSO. The simple PSO (sPSO) discards the particle velocity and reduces the bPSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. The extremum disturbed PSO (tPSO) accelerates the particles to overstep the local extremum. The experiment results of some classic benchmark functions show that the sPSO improves extraordinarily the convergence velocity and precision in the evolutionary optimization, and the tPSO can effectively break away from the local extremum. tsPSO, combined the sPSO and tPSO, can obtain the splendiferous optimization results with smaller population size and evolution generations. The algorithms improve the practicality of the particle swarm optimization.