Particle swarm optimization (PSO) algorithm is a new promising swarm intelligence optimization technology

and it has been extensively studied and applied because of its advantages of simpler theory

less parameters and better performance.However

each particle’s individual minimum has a low updating rate

which has been one disadvantageous factor to affect this algorithm speed and precision.In this paper

we propose a novel multi-step position-selectable updating PSO algorithm.This algorithm decomposes the standard PSO velocity single-step updating formula into three steps and selects the best one among the three resultant positions as the final updated position.This scheme refines each particle searching trajectory

increases the updating speed of individual and global minimums

and consequently improves PSO algorithm converging speed and precision without increasing the computing complexity.Six classical testing functions

including Sphere

Rosenbrock and so on

are used to verify the proposed algorithm in two ways:a fixed iteration number test and a fixed time length test.Large numbers of simulations show that the proposed algorithm is simple

robust

and efficient

and meanwhile it outperforms other four existing classical algorithms.