An adaptive particle swarm optimization algorithm with dynamic nonlinear inertia weight variation

In this paper, a novel particle swarm optimization algorithm based on a new dynamic nonlinear inertia weight variation model is proposed. The model is based on an exponent function formulate which may be more effectively to reflect the complicate search behavior of particles than previous variation inertia weight models. The purpose of this work to demonotrate is the development of an algorithm based on the new rules which improves the convergence ability of PSO. The key control parameters of the newly developed model were obtained through Sphere function tests. The novel PSO algorithm was tested on three well-known benchmark functions and the results were compared with the results of the two previous variation inertia weight PSO algorithms. The simulation results show that the present method outperforms the previous methods.