کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
385427 | 660865 | 2011 | 10 صفحه PDF | دانلود رایگان |
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.
Journal: Expert Systems with Applications - Volume 38, Issue 11, October 2011, Pages 14208–14217