Particle Swarm Optimization with varying Inertia Weight for solving nonlinear optimization problem

This paper focuses on performance studies of various variants of Inertia weights (w) of Particle Swarm Optimization (PSO). PSO is a metaheuristics optimization technique used for solving various complex optimization problems. It has various parameters to control its processing. Among those a very crucial one is Inertia Weight which is being used for controlling the velocity of the particle. In this paper a new concept of Inertia Weight is being introduced which is a function of previous inertia weight and is also dependent on previous local best values as well as global best values