Adaptive particle swarm optimization algorithm based on population velocity

The convergence of particle swarm optimization (PSO) algorithm is analyzed. Its premature convergence is due to the decrease of the velocity of particles in search space. An adaptive PSO algorithm with dynamical changing inertia weight based on population velocity is proposed. The information defined as the average absolute value of velocity of all particles is defined as information to change the inertia weight dynamicly, which can avoid the velocity 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 and can maintain the population diversity in the process of evolution.