摘要
研究了基于量子行为的微粒群优化(QPSO)算法在多阶段投资组合优化中制定投资决策的方法,目标函数是最大化个人经济效益或最大化周期结束时个人财富。通过比较用QPSO算法和遗传算法优化美国标准普尔指数100的不同股票和现金分配所得到的期望收益率均值与方差,证实了该方法的优越性。
The method of decision-making in the field of multi-stage portfolio optimization using Quantum-behaved Partical Swarm Optimization（QPSO） was studied. Its objective function was to maximize one＇s economic utility or end-of-period wealth. How to use QPSO to find best portfolio according to objective function was introduced. By comparing the expect return and their variances that come from optimizing the allocation of cash and various stocks in the market of USA, QPSO algorithm with genetic algorithms was demonstrated superior to genetic algorithms.
出处
《计算机应用》
CSCD
北大核心
2006年第7期1682-1685,1691,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(60474030)
关键词
粒子群优化
量子
多阶段投资组合
资产分配
partical-swarm-optimization
quantum
multi-stage-portfolio
asset allocation
