A New Particle Swarm Optimization with Random Parameters
Huang Shao-ron · Chongqing Shifan Daxue xuebao. Ziran kexue ban · 2013
Particle swarm optimization(PSO)is a powerful stochastic global technique,but easily trapped into local optimization,and its performance often depends heavily on the parameter settings.Based on analyzing the influence of the parameters setting in the experiment,this paper proposed a new particle swarm optimization algorithm which the inertia weight(ω)and acceleration coefficients(c1and c2)are generated as random numbers within a certain range in each iteration process:ω=rand(0.4,0.7),c1=rand(0.5,3.0),c2=rand(1.0,3.5).The proposed algorithms apply more particles' information,can easily jump out of local optimum and improve convergence performance.The experimental results demonstrate that the proposed algorithm is superior to the other two algorithms with a better astringency and stability.