摘要
微粒群优化(PSO)算法是新近出现的一种仿生算法,简单容易实现,而且随机搜索,不易陷于局部最优。本文将该算法引入证券投资组合领域,研究允许卖空证券和不允许卖空证券两种情形下的投资组合优化问题。文中首先系统介绍PSO算法原理、流程以及算法的改进发展,然后分析了卖空证券投资和不允许卖空证券投资两种情形下的优化模型,接下来介绍了应用PSO算法编码解决证券投资组合优化的方法步骤。最后,通过两个应用实例,计算表明PSO算法可以准确快速地解决证券投资组合优化问题。
The Particle Swarm Optimization （PSO）is an evolutionary computation, which not only can search solutions randomly and universally, but also is convenient to be carried out. And hence, the article focuses on the application of PSO to the portfolio problem, looking forward to seeking best solutions easily and quickly. After introducing the basic theory of the algorithms and its several versions, the article puts forward two models of portfolio problem. In the following parts, the article introduces how to apply PSO to solve the portfolio problem in detail. The numeric example followed indicates that PSO can solve the portfolio problem exactly and quickly.
出处
《系统管理学报》
北大核心
2008年第2期221-224,234,共5页
Journal of Systems & Management
基金
国家自然科学基金资助项目(70572043)
关键词
微粒群优化
证券组合投资问题
经济优化
particle swarm optimization （PSO）
portfolio problem
economic optimization
