Handling the complexities of the multi-constrained portfolio optimization problem with the support of a novel MOEA
针对多约束投资组合优化问题，提出一种新型多目标进化算法，包含高效表示方案和专门设计的变异与重组算子，在多达1317只股票的测试中优于两种知名算法。
The incorporation of additional constraints to the basic mean–variance (MV) model adds realism to the model, but simultaneously makes the problem difficult to be solved with exact approaches. In this paper we address the challenges that have arisen by the multi-constrained portfolio optimization problem with the assistance of a novel specially engineered multi-objective evolutionary algorithm (MOEA). The proposed algorithm incorporates a new efficient representation scheme and specially designed mutation and recombination operators alongside with efficient algorithmic approaches for the correct incorporation of complex real-world constraints into the MV model. We test the algorithm’s performance in comparison with two well-known MOEAs by using a wide range of test problems up to 1317 stocks. For all examined cases the proposed algorithm outperforms the other two MOEAs in terms of performance and processing speed.