A PSO algorithm with high speed convergence
Yongping Wu · Kongzhi yu juece · 2010
For the problem that particle swarm optimization (PSO) algorithm often suffers from being trapped in local optima so as to be premature convergence,an improved PSO with high-speed convergence is proposed to efficiently control premature stagnation.Firstly,chaotic sequence is used to initiate individual position,which strengthens the diversity of searching.Furthermore,an effective method that identifies premature stagnation is embedded to PSO,so once premature stagnation happens,a randomized solution,as a substitute for current optimum,is used to change the current searching locus so that particles can go out of the local optima.By using the two measures,the searching process can converge to the global optimum with high speed.Abundant simulation experiments demonstrate that the algorithm proposed in this paper only needs several particles and iterates a few times to be able to obtain the global optimum for most continuous function optimization problems.The convergence speed and searching ability are quite outstanding and satisfactory.