Multi-Objective Bayesian Optimization is a surrogate-assisted optimization method that extends Bayesian optimization to concurrently optimize multiple conflicting objectives by sequentially selecting evaluations using a probabilistic model and a multi-objective acquisition function.
150
Publications
6.5K
Citations
550
Authors
230
Institutions
Publications per year
2017–2026
136
Variants
Mobo
Publications
150
Multiobjective Tree-Structured Parzen Estimator
Yoshihiko Ozaki, Yuki Tanigaki, Shuhei Watanabe et al. · Journal of Artificial Intelligence Research · 2022 · 152 citations · Full text
1–5 of 150
Authors
550
- WL
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- AD
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- CY
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- DZ
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1–5 of 550
Institutions
230
University of Cambridge
Cambridge, United Kingdom
9 h-index
23 publications
768 citations
Texas A&M University
College Station, United States
5 h-index
23 publications
1.5K citations
Fudan University
Shanghai, China
5 h-index
26 publications
3.4K citations
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
15 publications
1.4K citations
University of California, Berkeley
Berkeley, United States
5 h-index
9 publications
314 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
4 h-index
14 publications
416 citations
University College London
London, United Kingdom
4 h-index
5 publications
106 citations
Huazhong University of Science and Technology
Wuhan, China
4 h-index
15 publications
675 citations
1–8 of 230
Venues
- arXiv (Cornell University)
Repository · 22 publications · 784 citations
15
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- Structural and Multidisciplinary Optimization
3 publications · 155 citations
3
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- Journal of Mechanical Design
3 publications · 65 citations
3
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- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
3 publications · 200 citations
3
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- Computers & Chemical Engineering
3 publications · 196 citations
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