Parallel quantum-behaved particle swarm optimization

被引：29
|
作者：
Tian, Na [1 ]
Lai, Choi-Hong [2 ]
机构：
[1] Jiangnan Univ, Dept Educ Technol, Wuxi 214122, Peoples R China
[2] Univ Greenwich, Sch Comp & Math Sci, London SE10 9LS, England
来源：
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2014年 / 5卷 / 02期
基金：
中国国家自然科学基金; 中央高校基本科研业务费专项资金资助;
关键词：
Quantum-behaved particle swarm optimization; Master-slave; Static subpopulation; Shape identification;
D O I：
10.1007/s13042-013-0168-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Quantum-behaved particle swarm optimization (QPSO), like other population-based algorithms, is intrinsically parallel. The master-slave (synchronous and asynchronous) and static subpopulation parallel QPSO models are investigated and applied to solve the inverse heat conduction problem of identifying the unknown boundary shape. The performance of all these parallel models is compared. The synchronous parallel QPSO can obtain better solutions, while the asynchronous parallel QPSO converges fast without idle waiting. The scalability of the static subpopulation parallel QPSO is not as good as the master-slave parallel model.
引用
收藏
页码：309 / 318
页数：10
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