Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
385427 | Expert Systems with Applications | 2011 | 10 Pages |
This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.
► We compare the effectiveness of five state-of-the-art multiobjective evolutionary algorithms on the mean variance cardinality constrained portfolio optimization problem. ► Methods are compared in a comprehensive computational experiment. ► The experiments showed a clear superiority of SPEA2. ► NSGA-II and SPEA2 are able to solve large-scale problems with up to 2196 assets.