Particle swarm optimization with dynamically changing inertia weight

To overcome the premature caused by standard particle swarm optimization (PSO) algorithm searching for the large lost in population diversity for the little search space, a dynamically changing inertia weight PSO based on the average distance with the best previously visited position and the position of the best individual of the whole swarm is proposed. The algorithm can balance the trade-off between exploration and exploitation and avoid prematurity. The simulation results show that the algorithm has high probability of finding global optimum and mean best value for multimodal function.