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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A novel hybrid heuristic algorithm for a new uncertain mean-variance-skewness portfolio selection model with real constraints
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A novel hybrid heuristic algorithm for a new uncertain mean-variance-skewness portfolio selection model with real constraints

机译：一种新的混合启发式算法，其具有真实约束的新不确定平均方差 - 偏斜组合选择模型

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摘要

This paper discusses a portfolio selection problem under the mean-variance-skewness framework wherein the security returns are obtained through evaluation of the experts instead of historical data. By treating security returns as the uncertain variables, an uncertain mean-variance-skewness model is proposed for portfolio selection under consideration of the transaction costs, bounds on holdings, cardinality of the portfolio, and minimum transaction lots constraints. To solve the resultant portfolio selection problem, which is an NP-Complete nonlinear integer programming problem, a hybrid solution method termed the FA-GA is developed by combining features of the firefly algorithm (FA) and genetic algorithm (GA). In the proposed method, the crossover and mutation operators of the GA are integrated into the FA to strike an optimal balance between the exploration and exploitation. A numerical example of portfolio selection is given to demonstrate effectiveness of the proposed model and solution algorithm. Furthermore, a detailed performance analysis and comparison are done to establish superiority of the proposed model and solution method.
机译：本文讨论了平均方差 - 偏斜框架下的投资组合选择问题，其中通过评估专家而不是历史数据来获得安全返回。通过将安全返回视为不确定的变量，提出了一个不确定的平均方差 - 偏斜模型，以考虑到交易成本，界限，投资组合的限制以及最小交易批次约束。为了解决基本组合选择问题，这是一个NP完整的非线性整数编程问题，通过组合萤火虫算法（FA）和遗传算法（GA）的特征来开发FA-Ga的混合解决方法。在所提出的方法中，GA的交叉和突变算子被整合到FA中，以在勘探和剥削之间取得最佳平衡。给出了投资组合选择的数值示例，以证明所提出的模型和解决方案算法的有效性。此外，完成了详细的性能分析和比较来建立所提出的模型和解决方法的优越性。

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