An improved particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm with the typical characteristic of swarm intelligence is a kind of novel evolution algorithm. It has been successfully applied to many optimization problems. But it can not give attention to convergence speed, capability of exploration and exploitation synchronously and it is easy to fall in local peak in large scale problem. To overcome these deficiencies, an improved particle swarm optimization algorithm is designed. This improved algorithm can enhance the capability of the global convergence and avoid failing into local optimization early. The results of the experiment show that the improved algorithm gets a better effect in comparison with the normal PSO.