[1]刘永波.投资组合优化的可行性规则人工蜂群算法[J].智能系统学报,2014,9(04):491-498.[doi:10.3969/j.issn.1673-4785.201308047]
 LIU Yongbo.An artificial bee colony algorithm with the feasibility rulefor portfolio investment optimizations[J].CAAI Transactions on Intelligent Systems,2014,9(04):491-498.[doi:10.3969/j.issn.1673-4785.201308047]
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投资组合优化的可行性规则人工蜂群算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年04期
页码:
491-498
栏目:
学术论文—智能系统
出版日期:
2014-08-25

文章信息/Info

Title:
An artificial bee colony algorithm with the feasibility rulefor portfolio investment optimizations
作者:
刘永波
泸州职业技术学院 信息工程系, 四川 泸州 646005
Author(s):
LIU Yongbo
Department of Information Engineering, Luzhou Vocational and Technical College, Luzhou 646005, China
关键词:
投资组合约束优化人工蜂群算法可行性规则Markov链
Keywords:
portfolio investmentconstrained optimizationartificial bee colony algorithmfeasibility ruleMarkov chain
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201308047
摘要:
给出含交易费用和投资者风险偏好的最佳证券投资组合约束优化模型, 并应用人工蜂群算法(ABC)求解该问题。应用可行性规则处理优化问题的约束条件, 形成可行性规则人工蜂群算法(FRABC)。应用Markov链理论证明FRABC算法为全局收敛算法。给出了证券投资组合优化仿真实例。实验结果表明, FRABC算法可行有效, 且寻优结果优于自适应遗传算法。在相同计算开销的条件下, FRABC算法的各项性能指标也明显好于遗传算法、粒子群算法及基本人工蜂群算法等对比算法。
Abstract:
This current work was carried out to approach the portfolio investment optimization problem by using an artificial bee colony (ABC) algorithm, in order to provide references for related researches. A constrained optimization model was constructed to formulate the portfolio investment optimization problem concerning securities subject to transaction fees and risk preferences of investors. This study employs feasibility rules to handle the constrained conditions of the optimization problem and forms an ABC algorithm with the feasibility rule (FRABC). It has been concluded by means of the Markov chain theory that the developed FRABC algorithm is globally convergent. A realistic case of the portfolio investment optimization is given to show that this method is valid and feasible, and the results are better than the ones obtained by the adaptive genetic algorithm (AGA). The proposed FRABC algorithm performs better, in terms of the final results, than the compared algorithms such as the genetic algorithm, particle swarm optimization algorithm and the basic ABC algorithm with the feasibility rule, under the assumed condition that the computational costs for the two algorithms are the same.

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备注/Memo

备注/Memo:
收稿日期:2013-08-29。
通讯作者:刘永波,男,1973年生,讲师,主要研究方向为计算机软件。主持青年基金项目1个、主研社科联课题2个,发表学术论文8篇,合作出版教材2部。E-mail: yongbo_liu@126.com
更新日期/Last Update: 1900-01-01