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A Comparative Study of Metaheuristics Techniques for Portfolio Selection Problem

机译：组合选择法元启发式技术的比较研究

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Portfolio selection problem (PSP) is one of the major interesting research areas in finance which have drawn interest of several researchers over the years. Over time, the different approaches had been engaged in solving the PSP ranging from computational techniques to metaheuristics techniques with varying results. In this paper, we engaged three different metaheuristics techniques under this same condition to solve extended Markowitz mean-variance portfolio selection model. The three metaheuristics techniques are Non-dominated Sorting Genetic Algorithm Ⅱ (NSGAⅡ), Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) and Generalized Differential Evolution 3 (GDE3). A comparative analysis was carried out with results obtained with existing benchmark data available in literature. The outcome of the findings reveals that SMPSO shows superior performance, followed by NSGAⅡ in many different instances; however, the mean execution time of GDE3 was the fastest among the three techniques considered.
机译：组合选择问题（PSP）是金融领域最重要的有趣研究领域之一，多年来引起了一些研究人员的兴趣。随着时间的流逝，从计算技术到元启发式技术，各种方法都用于解决PSP，但结果却有所不同。在本文中，我们在相同条件下采用了三种不同的元启发式技术来解决扩展的Markowitz均值-方差投资组合选择模型。三种元启发式技术分别是非支配排序遗传算法Ⅱ（NSGAⅡ），速度受限的多目标粒子群优化算法（SMPSO）和广义差分进化3（GDE3）。使用文献中现有的基准数据获得的结果进行了比较分析。研究结果表明，SMPSO表现出优异的性能，在许多不同的情况下紧随其后的是NSGAⅡ。但是，GDE3的平均执行时间是所考虑的三种技术中最快的。

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