A Simplified Adaptive Particle Swarm Optimization Algorithm

摘要

A new particle swarm optimization (PSO) algorithm is presented based on three methods of improvement in original PSO. First, the iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution. Second, the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm. Finally, the mutation operator is introduced to improve the search performance of algorithm. Experimental results show that the new algorithm not only outperforms standard PSO in terms of accuracy and convergence rate but also avoids effectively being trapped in local minima.