A Particle Swarm Optimization Algorithm Based on Dynamic Intertia Weight

The normal PSO algorithm is a validated evolutionary computation way of searching the extremum of function,which is simple in application and quick in convergence,but low in precision and easy in premature convergence.Because of the limitation,a dynamically changing inertia weight PSO algorithm is proposed based on the normal PSO algorithm.The inertia weight is changed in every loop according to the swarm evolution degree and aggregation degree factor.Compared with ACWPSO and the normal PSO,the optimization results of testing function show that the performance of the DCWPSO algorithm is more excellent.