Multiobjective Extremal Optimization for Portfolio Optimization Problem

被引：4
|
作者：
Chen, Min-Rong [1 ]
Weng, Jian [2 ]
Li, Xia [1 ]
机构：
[1] Shenzhen Univ, Coll Informat Engn, Shenzhen, Peoples R China
[2] Jinan Univ, Dept Comp Sci, Guangzhou, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年
基金：
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词：
multiobjective optimization; multiobjective extremal optimization; portfolio optimization problem; ALGORITHMS;
D O I：
10.1109/ICICISYS.2009.5357781
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Portfolio optimization plays a critical role in determining portfolio strategies for investors and it is intrinsically a discrete multiobjective optimization problem whose decision criteria conflict with each other This paper extends a novel numerical multiobjective optimization algorithm, so-called Multiobjective Extremal Optimization (MOEO), to solve the portfolio optimization problem The proposed approach is validated by five popular stock indexes The simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i e NSGA-II, SPEA2 and PAES Thus, MOEO can be considered a good alternative to solve portfolio optimization problem
引用
收藏
页码：552 / +
页数：2
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