Hybrid metaheuristic for Portfolio Selection: Comparison with an exact solver and search space analysis

Abstract

In this paper we use a metaheuristic approach to solve the Portfolio Selection problem, in a constrained formulation which is NP-hard and difficult to be solved by standard optimization methods. We are comparing the algorithm's performances with an exact solver and we are showing that different mathematical formulations lead to different algorithm's behaviour. Results show that our approach can be efficiently used to solve the problem at hand, and that a sound basin of attraction analysis may help developers and practitioners to design the experimental analysis.