Application of the Improved Differential Evolution Algorithm in Portfolio

被引：0
|
作者：
Ning, Gui-Ying [1 ]
Cao, Dun-Qian [2 ]
Zhou, Yong-Quan [3 ]
机构：
[1] Guangxi Univ Sci & Technol, Lushan Coll, Liuzhou 545616, Guangxi, Peoples R China
[2] Guangxi Univ Nationalities, Coll Math & Comp Sci, Nanning 530006, Peoples R China
[3] Guangxi Univ Nationalities, Coll Informat Sci & Engn, Nanning 530006, Guangxi, Peoples R China
来源：
2017 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (IST 2017) | 2017年 / 11卷
基金：
中国国家自然科学基金;
关键词：
OPTIMIZATION;
D O I：
10.1051/itmconf/20171101006
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Aiming at the NP hard problem of portfolio optimization, an improved differential evolution algorithm is proposed. In this algorithm, the mutation operator and crossover operator are set up adaptively, and then according to the characteristics of the mutation itself, two kinds of mutation operators with global search ability and local search ability are improved .The improved algorithm can improve the convergence speed and ensure the precision of the algorithm. Through five stocks of the same type and 20 different types of stocks for empirical analysis, the results show that the proposed algorithm has a certain guiding role in solving the problem of portfolio optimization.
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收藏
页数：9
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