Abstract: Aiming at some demerits of particle swarm optimization algorithm(PSO),such as relapsing into local extremum easily,slow convergence velocity and low convergence precision in the late evolutionary,an improved simple particle swarm optimization algorithm(YSPSO) was proposed.It employs golden section method to balance the mutual in-fluence between inertia and experience.Meanwhile,in order to avoid missing the global optimal value,it adds reverse random inertia weights to make the particles have the ability to search reversely in a certain extent.Finally,the experiment results of several classic benchmark functions show that YSPSO improves the practicability of PSO via improving convergence velocity and precision,and reducing the possibility of relapsing into local extremum.

Key words: Swarm intelligence,Particle swarm optimization,Golden section method