Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
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DOI：
10.1016/j.physa.2015.02.060
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发表时间：
2015-07-01
影响因子：
3.3
通讯作者：
Chen, Wei
中科院分区：
文献类型：
--
作者：
Chen, Wei
本文在风险资产收益率为模糊数的假设下，讨论了具有现实约束的投资组合优化问题。提出了一个新的可能性均值-半绝对离差模型，该模型考虑了交易费用、基数和数量约束。由于这些约束条件，该模型成为一个混合整数非线性规划问题，传统的优化方法无法有效地找到最优解。因此，改进的人工蜂群（MABC）算法被开发来解决相应的优化问题。最后通过一个算例说明了所提模型和相应算法的有效性。(C)2015 Elsevier B. V.版权所有。
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm. (C) 2015 Elsevier B.V. All rights reserved.