Portfolio Optimization Using Non-linear Inertia Weight PSO

摘要

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 PSO with non-linear inertia weight based on the tangent function is provided. The performance of the proposed PSO models is compared with standard PSO with linearly-decrease inertia weight to solve an improved portfolio optimization model with complex constraints. The experimental results demonstrate that that the proposed PSO model is better than standard PSO in terms of convergence rate and solution precision.