粒子群算法在金融风险模型中的研究与改进  

Research and Improvement of Particle Swarm Optimization in Financial Risk Model

作　　者：孙艺[1] 宋振铭 赵佳琪 SUN Yi;SONG Zhenming;ZHAO Jiaqi(Software College,Beijing University of Posts and Telecommunications,Beijing 100876,China;Standard Evaluation Department,China National Institute of Standardization,Beijing 100191,China)

机构地区：[1]北京邮电大学软件学院,北京100876 [2]中国标准化研究院标准评估部,北京100191

出　　处：《吉林大学学报（信息科学版）》2020年第2期192-198,共7页Journal of Jilin University（Information Science Edition）

基　　金：中央高校基本科研业务费专项基金资助项目(2019RC52)。

摘　　要：在一种非线性金融风险模型中引入粒子群算法,针对粒子群算法在迭代后期搜索能力不高、粒子容易陷入局部最优的问题,基于对惯性权重的优化以及对每个粒子个体位置变异,提出一种改进后的粒子群算法。利用粒子群算法选择最优控制参数,以最大程度降低金融系统的总风险值。仿真结果表明,改进后的粒子群算法在全局最优以及搜索速度方面优于传统的粒子群算法。Particle swarm optimization algorithm is introduced into a nonlinear financial risk model to solve the problem that particle swarm optimization algorithm has low search ability and particle is easy to fall into local optimization at the later stage of iteration.based on the optimization of inertia weight and the variation of individual position of each particle,an improved particle swarm optimization algorithm is proposed.PSO(Particle Swarm Optimization)is used to select the optimal control parameters to reduce the total risk value of the financial system to the greatest extent.The simulation results show that the improved particle swarm optimization algorithm is superior to the traditional particle swarm optimization algorithm in terms of global optimization and search speed.

关 键 词：非线性 粒子群 风险控制 全局最优 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]