An improved particle swarm optimization based on difference equation analysis

With the development of science and technology, optimization methods for complicated optimization problems which having high real-time requirement are getting more and more attentions. Particle swarm optimization (PSO) is fit for solving such optimization problems. But PSO still need to speed up convergence rate and avoid being trapped into local optimal solution as possible when solving complicated multimodal optimization problems. An improved PSO algorithm based on self-adaptive inertia weight was proposed in this paper. Firstly, the convergence conditions of PSO algorithm were derived based on difference equation analysis. Secondly, set the value of cognition factor and social factor as function of inertia weight under the direction of convergence conditions. It is shown in the experiment results that the optimization performance of the improved PSO algorithm proposed in this paper is better than three other outstanding PSO algorithms. The improved PSO algorithm will have a promising application prospect in optimization areas which need high real-time performance.