Improved Particle Swarm Optimizers with Application on Constrained Portfolio Selection

Inertia weight is one of the most important adjustable parameters of particle swarm optimization (PSO). The proper selection of inertia weight can prove a right balance between global search and local search. In this paper, a novel PSOs with non-linear inertia weight based on the arc tangent function is provided. The performance of the proposed PSO models are compared with standard PSO with linearly-decrease inertia weight using four benchmark functions. The experimental results demonstrate that our proposed PSO models are better than standard PSO in terms of convergence rate and solution precision. The proposed novel PSOs are also used to solve an improved portfolio optimization model with complex constraints and the primary results demonstrate their effectiveness.