Inertia Weight Adaption in Particle Swarm Optimization Algorithm

In Particle Swarm Optimization (PSO), setting the inertia weight w is one of the most important topics. The inertia weight was introduced into PSO to balance between its global and local search abilities. In this paper, first, we propose a method to adaptively adjust the inertia weight based on particle's velocity information. Second, we utilize both position and velocity information to adaptively adjust the inertia weight. The proposed methods are then tested on benchmark functions. The simulation results illustrate the effectiveness and efficiency of the proposed algorithm by comparing it with other existing PSOs.