改进型粒子群算法及在证券投资中的应用  

Improved Particle Swarm Optimization Algorithm and Its Application in Securities Investment

作　　者：何光[1] 

机构地区：[1]内江师范学院数学与信息科学学院//四川省高等学校数值仿真重点实验室,四川内江641100

出　　处：《内江师范学院学报》2012年第10期24-27,共4页Journal of Neijiang Normal University

摘　　要：在证劵市场中有很多不连续的投资优化模型,为了快速有效的寻求模型的解,设计了一种改进的优化算法.通过引入遗传算法中的交叉操作,得到了基于最优和次优位置的改进粒子群优化算法(INPSO).在性能检测中,该算法比部分改进的粒子群优化算法表现更佳,克服了早熟的缺陷.然后,在仿真实验中,运用INPSO分别获得了两种投资组合模型在不同期望收益率下的优化值,同时算法在迭代过程中展现出了很好的收敛性.In order to seek the solutions of many discontinuous portfolio optimization models in stock market promptly and efficiently, an improved optimization algorithm was designed. By introducing crossover operations in genetic algorithm, an innovative particle swarm optimization algorithm （INPSO） based on optimal and suboptimal locations was proposed. And a per- formance test reveals that the algorithm performs better than some improved particle swarm optimization algorithms,and over- comes the problem of prematurity. Then in simulation experiment, by use of INPSO the optimal values of two portfolio models under different expected return rates were thus obtained, and the algorithm shows a marvelous convergence property in the iter- ation process.

关 键 词：粒子群优化算法 交叉操作 投资组合 期望收益率 优化模型 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]