Localized probability of improvement for kriging based multi-objective optimization
The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationall...
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De Gruyter
2017-12-01
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| Series: | Open Physics |
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| Online Access: | https://doi.org/10.1515/phys-2017-0117 |
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doaj-b30986e067414a19b2b0fdca50cb72d52021-09-05T13:59:34ZengDe GruyterOpen Physics2391-54712017-12-0115195495810.1515/phys-2017-0117phys-2017-0117Localized probability of improvement for kriging based multi-objective optimizationLi Yinjiang0Xiao Song1Barba Paolo Di2Rotaru Mihai3Sykulski Jan K.4Electronics and Computer Science, University of Southampton, SouthamptonSO17 1BJ, United Kingdom of Great Britain and Northern IrelandElectronics and Computer Science, University of Southampton, SouthamptonSO17 1BJ, United Kingdom of Great Britain and Northern IrelandDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, ItalyElectronics and Computer Science, University of Southampton, SouthamptonSO17 1BJ, United Kingdom of Great Britain and Northern IrelandElectronics and Computer Science, University of Southampton, SouthamptonSO17 1BJ, United Kingdom of Great Britain and Northern IrelandThe paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.https://doi.org/10.1515/phys-2017-0117krigingmulti-objective optimizationpareto frontsurrogate-based optimization89.20.kk85.85.+j85.70.-w87.10.mn |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Li Yinjiang Xiao Song Barba Paolo Di Rotaru Mihai Sykulski Jan K. |
| spellingShingle |
Li Yinjiang Xiao Song Barba Paolo Di Rotaru Mihai Sykulski Jan K. Localized probability of improvement for kriging based multi-objective optimization Open Physics kriging multi-objective optimization pareto front surrogate-based optimization 89.20.kk 85.85.+j 85.70.-w 87.10.mn |
| author_facet |
Li Yinjiang Xiao Song Barba Paolo Di Rotaru Mihai Sykulski Jan K. |
| author_sort |
Li Yinjiang |
| title |
Localized probability of improvement for kriging based multi-objective optimization |
| title_short |
Localized probability of improvement for kriging based multi-objective optimization |
| title_full |
Localized probability of improvement for kriging based multi-objective optimization |
| title_fullStr |
Localized probability of improvement for kriging based multi-objective optimization |
| title_full_unstemmed |
Localized probability of improvement for kriging based multi-objective optimization |
| title_sort |
localized probability of improvement for kriging based multi-objective optimization |
| publisher |
De Gruyter |
| series |
Open Physics |
| issn |
2391-5471 |
| publishDate |
2017-12-01 |
| description |
The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions. |
| topic |
kriging multi-objective optimization pareto front surrogate-based optimization 89.20.kk 85.85.+j 85.70.-w 87.10.mn |
| url |
https://doi.org/10.1515/phys-2017-0117 |
| work_keys_str_mv |
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| _version_ |
1717813427328516096 |