A Dynamic Inertia Weight Particle Swarm Optimizer Algorithm
Lili Li · Journal of Guangxi Normal University · 2008
Particle swarm optimization(PSO) algorithm has been developing rapidly and has been applied widely since it was proposed,as it has rapid convergence velocity and can be easily realized.In this paper,an improved particle swarm optimization algorithm is introduced.In order to improve convergence velocity to avoid decreasing rapidly in the later period evolution,the algorithm uses the dynamic inertia weight that non-linear decrease with iterative generation increasing.To study the performance of the algorithm,it is tested with a set of 5 benchmark functions and compared with the linear decrease weight particle swarm optimization algorithm.Numerical simulation results show that the improved algorithm can improve the search performance on the benchmark functions remarkably.