Comparative Performance of Surrogate-Assisted MOEAs for Geometrical Design of Pin-Fin Heat Sinks
This paper presents the comparative performance of several surrogate-assisted multiobjective evolutionary algorithms (MOEAs) for geometrical design of a pin-fin heat sink (PFHS). The surrogate-assisted MOEAs are achieved by integrating multiobjective population-based incremental learning (PBIL) with...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/534783 |
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