Quantum-behaved Particle Swarm Optimization with Cooperative Coevolution for Large Scale Optimization

被引：7
|
作者：
Tian, Na [1 ]
机构：
[1] Jiangnan Univ, Dept Educ Technol, Wuxi 214122, Peoples R China
来源：
14TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS, ENGINEERING AND SCIENCE (DCABES 2015) | 2015年
关键词：
large scale; quantum-behaved particle swarm optimization; cooperative coevolution; domain decompositimponent;
D O I：
10.1109/DCABES.2015.28
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Quantum-behaved particle swarm optimization (QPSO) has successfully been applied to unimodal and multimodal optimization problems. However, with the emerging and popular of big data and deep machine learning, QPSO encounters limitations with high dimensions. In this paper, QPSO with cooperative coevolution (QPSO_CC) is used to decompose the high dimensional problems into several lower dimensional problems and optimize them separately. The numerical experimental results show that QPSO_CC has comparative or even better performance than other algorithms.
引用
收藏
页码：82 / 85
页数：4
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