期刊
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
卷 5, 期 2, 页码 309-318出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-013-0168-2
关键词
Quantum-behaved particle swarm optimization; Master-slave; Static subpopulation; Shape identification
资金
- Bursary of the University of Greenwich, The Fundamental Research Funds for the Central Universities'' [JUSRP21012]
- innovative research team project of Jiangnan University [JNIRT0702]
- National Natural Science Foundation of China [60973094]
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.
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