[1]LIU Yongbo.An artificial bee colony algorithm with the feasibility rulefor portfolio investment optimizations[J].CAAI Transactions on Intelligent Systems,2014,9(4):491-498.[doi:10.3969/j.issn.1673-4785.201308047]
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An artificial bee colony algorithm with the feasibility rulefor portfolio investment optimizations

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 4 Page number: 491-498 Column: 学术论文—智能系统 Public date: 2014-08-25
Title:
An artificial bee colony algorithm with the feasibility rulefor portfolio investment optimizations
Author(s):
LIU Yongbo
Department of Information Engineering, Luzhou Vocational and Technical College, Luzhou 646005, China
Keywords:
portfolio investment; constrained optimization; artificial bee colony algorithm; feasibility rule; Markov chain
CLC:
TP301.6
DOI:
10.3969/j.issn.1673-4785.201308047
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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