Analysis and dynamical changing inertia weight strategy of particle swarm optimization

Convergence of particle velocity and effect on optimization performance were analyzed in particle swarm optimization, and a new algorithm with dynamical changing inertia weight was proposed. The information defined as the average absolute value of velocity of all particles was used in the algorithm, which can avoid premature convergence for the velocity is closed to 0 in the early search part. The simulation results show that the algorithm has better probability of finding global optimum and mean best value than other algorithms for maintaining the population diversity.