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Parallel quantum-behaved particle swarm optimization
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DOI：10.1007/s13042-013-0168-2.png)
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Abstract
En 中文
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.
Keywords:
Quantum-behaved particle swarm optimization
Master-slave
Static subpopulation
Shape identification
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