基于元胞蚂蚁算法的商业银行信用风险评估模型研究

发布时间：2018-06-24 23:19

  本文选题：蚂蚁算法 + 元胞自动机　； 参考：《上海金融》2017年01期

【摘要】：蚁群优化算法是一种新型的解决组合优化问题的仿真型算法,在许多优化计算领域中都有广泛的应用,但却有容易陷入局部最优等方面的缺陷。本人之前研究将元胞自动机原理引入蚂蚁算法,构造元胞蚂蚁算法,该算法从理论上被证明能部分避免蚂蚁算法的缺陷从而提高算法的有效性。本文针对商业银行信用风险评估模型的特性,对元胞蚂蚁算法的选择策略、元胞机制、信息素更新机制及寻优终止条件等机制进行改造,提出一种具备自我学习和系统信息反馈机制的商业银行信用风险评估元胞蚂蚁算法模型。最后用DELPHI做了算法仿真实验,结果验证了该模型在信用风险评估中具有较好准确性。
[Abstract]:Ant colony optimization (ACO) algorithm is a new simulation algorithm for combinatorial optimization problems. It is widely used in many fields of optimization, but it is easy to fall into local optimization. In this paper, the principle of cellular automata is introduced into ant algorithm to construct a cellular ant algorithm. This algorithm has been proved theoretically to partially avoid the defect of ant algorithm and improve the effectiveness of the algorithm. In this paper, according to the characteristics of credit risk assessment model of commercial banks, the selection strategy, cellular mechanism, pheromone updating mechanism and optimization termination condition of the Cellular Ant algorithm are modified. A cellular ant algorithm model for credit risk assessment of commercial banks with self-learning and systematic information feedback mechanism is proposed. Finally, the algorithm is simulated with Delphi, and the results show that the model is accurate in credit risk assessment.
【作者单位】： 复旦大学应用经济学流动站;上海市高级人民法院;
【分类号】：F832.4
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本文编号：2063432