A Novel Quantum-behaved Particle Swarm Optimization Algorithm

被引：1
|
作者：
Zhao, Jing [1 ,2 ]
Liu, Hong [3 ]
机构：
[1] Qilu Univ Technol, Sch Informat, Jinan, Shandong, Peoples R China
[2] Shandong Normal Univ, Shandong Prov Key Lab Distributed Comp Software, Jinan, Peoples R China
[3] Shandong Normal Univ, Shandong Prov Key Lab Distributed Comp Software, Coll Informat Sci & Engn, Jinan, 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：
particle swarm optimization algorithm; quantum-behaved; probability; benchmark function;
D O I：
10.1109/DCABES.2015.31
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A novel Quantum-behaved Particle Swarm Optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.
引用
收藏
页码：94 / 97
页数：4
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