A Novel Particle Swarm Optimization with Non-linear Inertia Weight Based on Tangent Function

Inertia weight is a most important parameter of particle swarm optimization (PSO), which can keep a right balance between the global search and local search. In this paper, a novel PSO with non-linear inertia weight based on the tangent function is provided. The paper also presents the method of determining a control parameter in our proposed method, saving the user from a tedious trial and error based approach to determine it for each specific problem. The performance of the proposed PSO model is amply demonstrated by applying it for four benchmark problems and comparing it with other three PSO algorithms. From experimental results, it can be concluded that using a non-linear dynamic inertia weight makes the rapidity of convergence rate with higher precision.