计算机工程与应用2024，Vol.60Issue(19)：309-322,14.DOI:10.3778/j.issn.1002-8331.2402-0046
面向高维投资组合的多目标优化算法
Multi-Objective Optimization Algorithm for High-Dimensional Portfolios
摘要
Abstract
Addressing high-dimensional portfolio optimization problems,this paper introduces a multi-objective evolu-tionary algorithm based on nondominated sorting and hybrid search that integrates decomposition methods and multiple subpopulation strategies.Considering the limitations of existing evolutionary algorithms in dealing with large-scale prob-lems due to their expansive search spaces,a decomposition-based strategy is introduced.This strategy effectively divides the population into three subgroups by analyzing the distance between individuals and reference points.To enhance popu-lation diversity and avoid local optima,the algorithm incorporates individual positional characteristics and utilizes a hy-brid of local and global search strategies.Furthermore,the algorithm effectively generates high-quality solutions through a decomposition-based dual-environment selection mechanism.In LSMOP experiments with 100,500,and 1 000 decision variables,the algorithm demonstrates performance surpassing several advanced evolutionary algorithms.Lastly,applying this algorithm to the CVaR model with transaction costs and comparing it with three other multi-objective evolutionary al-gorithms further confirms its advantages in practical applications.关键词
多目标优化/进化算法/非支配排序/混合搜索Key words
multi-objective optimization/evolutionary algorithm/nondominated sorting/hybrid search分类
信息技术与安全科学引用本文复制引用
宋英杰,韩礼欢..面向高维投资组合的多目标优化算法[J].计算机工程与应用,2024,60(19):309-322,14.基金项目
国家自然科学基金(62341605) （62341605）
山东省自然科学基金(ZR2023MF084). （ZR2023MF084）
