Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 54, Issue 4, Pages 747-773Publisher
SPRINGER
DOI: 10.1007/s00158-016-1441-2
Keywords
Efficient global optimization; Parallel computing; Multiple points infill criterion; Mutual information; Expected improvement
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Funding
- National Natural Science Foundations of China [11432003]
- National 973 Project of China [2012CB025905]
- National Natural Science Funds of China [11202049, 11402049]
- National High Technology Research and Development Program of China [2015AA033803]
- China Postdoctoral Science Foundation [2015 M571298]
- Fundamental Research Funds for the Central Universities of China [DUT14RC(3)060]
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This work is focused on the parallel algorithms in efficient global optimization. Firstly, a multiple points infill criterion named EI&MI is developed, which adopts the entropy to precisely measure the uncertainty of Kriging surrogate, and then balances global exploration and local exploitation of the multiple points infill sampling criteria. Secondly, given the computational difficulties in Kriging with a large size of training data, a domain decomposition optimization strategy is proposed, which ensures a small size of training data. Several mathematical functions and one engineering problem are employed as testing examples. The results show that comparing with several other methods, the EI&MI has an obvious advantage in solving complex optimization problems under the large-scale parallel computing environment, and the domain decomposition optimization strategy could improve the stability of optimization without sacrificing the optimization efficiency.
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