.png){kind=icon}
返回
A modified particle swarm optimization algorithm with applications
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2012.07.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, firstly a modified particle swarm optimization algorithm (MPSO) is developed, in which the mean value of past optimal positions for each particle and the mutation operation are considered for avoiding premature. In the optimization test, MPSO performs better than particle swarm optimization algorithm (PSO). Then MPSO is applied to solve four portfolio optimization models with the real data from the Hong Kong Stock Market, and optimal values are obtained when the number of swarm n = 80; 160, respectively. Finally, actual return rates of these models are calculated in numerical experiments, and it is illustrated from these graphs of actual return rates that when considering higher return, Cai's model performs better in short-term investment. (C) 2012 Elsevier Inc. All rights reserved.
Keyword:
Modified particle swarm optimization algorithm
Mutation operation
Optimal position
Portfolio optimization model
Actual return rate
Numerical experiment
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏From stochastic dominance to mean-risk models: Semideviations as risk measures从随机优势到均值风险模型: 作为风险度量的半概念
OAAI
OAAI分享收藏Comparison of clinical characteristics in obsessive-compulsive disorder and body dysmorphic disorder
PREAI
分享收藏
查看更多内容