A Particle Swarm Optimization Algorithm with Initialized Adaptive Inertia Weights

An improved particle swarm optimization (PSO) algorithm with initialized adaptive inertia weights is proposed. An adaptive inertia weight strategy is introduced into the algorithm to balance the global and the local search abilities. In the evolution process, the swarm trapping at the stagnation is reinitialized around its weighted centroid position, which guides the swarm to avoid local extremum and accelerates the convergence rate of the algorithm. The comparison among the performances of the proposed approach, the standard PSO and the LDW-PSO is done. The experiment results show that the proposed algorithm can not only enhance the ability of avoiding local extremum, but also speed up the convergence rate and improve the stability to a certain degree.