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Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection
Gao, Weifeng1; Sheng, Hailong2; Wang, Jue3,4; Wang, Shouyang3,4
2019-05-01
Source PublicationIEEE TRANSACTIONS ON FUZZY SYSTEMS
ISSN1063-6706
Volume27Issue:5Pages:966-978
AbstractAlthough the introduction of fuzzy theory into a portfolio selection model can help improve the model's practicality, it would increase the difficulty of solving the model. To tackle the issue, this paper proposes a novel mechanism based artificial bee colony algorithm (ABC) consisting of two new proposed learning strategies-direction learning and elite learning. The direction learning strategy has a great potential to guide the search toward the promising areas. The elite learning strategy can gradually pick up the convergence rate without loss of the population diversity. The cooperation of the two approaches forms a mechanism, complementing each other to improve the performance of the algorithms. The proposed mechanism, named LL-mechanism, is introduced into three ABC variants-ABC, ghest-guided ABC (GABC), and CABC, generating LL-ABC, LL-GABC, and LL-CABC, respectively. The experimental results demonstrate the superior performance of the LL-mechanism and LL-CABC outperforms other methods in terms of solution quality, convergence rate, robustness, and numerical stability. Finally, the proposed LL-CABC approach is employed to solve the portfolio selection with fuzzy security return. The experiments on two portfolio selection models illustrate that LL-CABC is effective and promising for a fuzzy portfolio selection.
KeywordArtificial bee colony algorithm (ABC) direction learning elite learning fuzzy portfolio selection search direction step size
DOI10.1109/TFUZZ.2018.2856120
Language英语
Funding ProjectNational Nature Science Foundation of China[61772391] ; National Nature Science Foundation of China[61402534] ; National Nature Science Foundation of China[71771208] ; National Nature Science Foundation of China[71271202] ; Natural Science Basic Research Plan in Shaanxi Province of China[2018JQ6051] ; Natural Science Basic Research Plan in Shaanxi Province of China[2017JQ6059] ; Young Talent fund of University Association for Science and Technology in Shaanxi, China, Hong Kong Scholars Program
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000470836800012
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/34822
Collection系统科学研究所
Corresponding AuthorWang, Jue
Affiliation1.Xidian Univ, Sch Math & Stat, Xian 710126, Shaanxi, Peoples R China
2.China Univ Petr, Sch Sci, Qingdao 266580, Shandong, Peoples R China
3.Univ Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Gao, Weifeng,Sheng, Hailong,Wang, Jue,et al. Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2019,27(5):966-978.
APA Gao, Weifeng,Sheng, Hailong,Wang, Jue,&Wang, Shouyang.(2019).Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection.IEEE TRANSACTIONS ON FUZZY SYSTEMS,27(5),966-978.
MLA Gao, Weifeng,et al."Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection".IEEE TRANSACTIONS ON FUZZY SYSTEMS 27.5(2019):966-978.
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