Hybrid Metaheuristics for Constrained Portfolio Selection Problems

none4Portfolio selection is a relevant problem arising in finance and economics. While its basic formulations can be efficiently solved through linear or quadratic programming, its more practical and realistic variants, which include various kinds of constraints and objectives, have in many cases to be tackled by heuristics. In this work, we present a hybrid technique that combines a local search metaheuristic, as master solver, with a quadratic programming procedure, as slave solver. Experimental results show that the approach is very promising as it reaches regularly the optimal solution and thus achieves results comparable with, or superior to, the state of the art solvers, including widespread commercial software tools (CPLEX 11.0.1 and MOSEK 5). Finally, the paper proposes a detailed analysis of the behavior of the technique in various settings of the constraints, thus showing how the performances are dependent on the features of the instance.noneL. Di Gaspero; A. Schaerf; G. di Tollo; A. RoliL. Di Gaspero; A. Schaerf; G. di Tollo; A. Rol