论文标题
使用MOEA/D和LEVY FLIGHT解决产品组合优化问题
Solving Portfolio Optimization Problems Using MOEA/D and Levy Flight
论文作者
论文摘要
投资组合优化是一项财务任务，需要在一组金融资产上分配资本，以在回报和风险之间取得更好的权衡。为了解决这一问题，最近的研究应用了多目标进化算法（MOEAS）的自然双目标结构。本文提出了一种将基于分布的突变方法注入莱维飞行的方法，该方法将基于分解的MOEA分解为MOEA/d。将所提出的算法与三种MOEA/D类算法，NSGA-II和其他基于分布的突变方法进行了五个投资组合优化基准在没有约束的情况下，在没有约束的情况下进行的五个投资组合优化基准，评估了六个指标。数值结果和统计测试表明，在大多数情况下，此方法可以胜过比较方法。我们分析了Levy飞行如何通过优化的早期促进全球搜索来促进这一改进。我们通过考虑突变方法与问题的特性之间的相互作用来解释这一改进。
Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk. To solve this problem, recent studies applied multi-objective evolutionary algorithms (MOEAs) for its natural bi-objective structure. This paper presents a method injecting a distribution-based mutation method named Lévy Flight into a decomposition based MOEA named MOEA/D. The proposed algorithm is compared with three MOEA/D-like algorithms, NSGA-II, and other distribution-based mutation methods on five portfolio optimization benchmarks sized from 31 to 225 in OR library without constraints, assessing with six metrics. Numerical results and statistical test indicate that this method can outperform comparison methods in most cases. We analyze how Levy Flight contributes to this improvement by promoting global search early in the optimization. We explain this improvement by considering the interaction between mutation method and the property of the problem.
