Symbiotic Multi-swarm PSO for Portfolio Optimization

被引:0
作者
Niu, Ben [1 ]
Xue, Bing [1 ]
Li, Li [1 ]
Chai, Yujuan [2 ]
机构
[1] Shenzhen Univ, Coll Management, Shenzhen 518060, Guangdong, Peoples R China
[2] McMaster Univ, Fac Sci, Hamilton, ON L8S 4L8, Canada
来源
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2009年 / 5755卷
关键词
Symbiotic PSO; particle swarm; portfolio optimization; TRACKING ERROR MINIMIZATION; PROGRAMMING SOLUTION; SELECTION; MANAGEMENT; SUPPORT;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a novel symbiotic multi-swarm particle swarm optimization (SMPSO) based on our previous proposed multi-swarm cooperative particle swarm optimization. In SMPSO, the population is divided into several identical sub-swarms and a center communication strategy is used to transfer the information among all the sub-swarms. The information sharing among all the sub-swarms can help the proposed algorithm avoid be trapped into local minima as well as improve its convergence rate. SMPSO is then applied to portfolio optimization problem. To demonstrate the efficiency of the proposed SMPSO algorithm, an improved Markowitz portfolio optimization model including two of the most important limitations are adopted. Experimental results show that SMPSO is promising for this class of problems.
引用
收藏
页码:776 / +
页数:2
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