Solving Portfolio Optimization Problems Using MOEA/D and Levy Flight
This is an incremental improvement for financial portfolio optimization, enhancing trade-offs between return and risk.
The paper tackled portfolio optimization by injecting Lévy Flight mutation into MOEA/D, resulting in outperformance over several comparison methods on benchmarks up to 225 assets in most cases, as shown by numerical results and statistical tests.
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.