A self-adaptive integrated particle swarm optimization
The particle swarm optimization (PSO) algorithm is a new and efficient intelligent search algorithm. However, most of the existing improved PSO algorithm in the late search efficiency is low, and easy fall into local minima, In view of this situation, a kind of new strategy of inertia weight adaptive changes been proposed in this paper, which enable the algorithm in the interim iterations can quick access into the local search, conductive to facilitate the search for the best value. And in order to prevent the algorithm from falling into local extreme value, we joined the disturbance factor and the improved acceleration factor. Using five kinds of classic test functions for performance test of the improved algorithm, the test results indicate that the improved algorithm can get better value and can avoid falling into local minimum too early.