Multi-objective Surrogate Modeling

Multi-Objective Surrogate Modeling is a surrogate modeling approach that approximates multiple conflicting objective functions simultaneously, enabling efficient optimization in computationally expensive multi-objective problems by replacing costly high-fidelity evaluations with fast-to-evaluate metamodels.

217

Publications

10.2K

Citations

732

Authors

283

Institutions

Publications per year

2017–2026

135

Authors

732

  1. LG
    Liang Gao

    Huazhong University of Science and Technology, China

    5 publications · 191 citations

    5

    h-index

  2. SO
    Shigeru Obayashi

    Tohoku University, Japan

    4 publications · 199 citations

    4

    h-index

  3. HD
    Huachao Dong

    Northwestern Polytechnical University, China

    4 publications · 173 citations

    4

    h-index

  4. PW
    Peng Wang

    Northwestern Polytechnical University, China

    4 publications · 173 citations

    4

    h-index

  5. IC
    Ivo Couckuyt

    Ghent University, Belgium

    4 publications · 148 citations

    4

    h-index

1–5 of 732

Venues

  1. Structural and Multidisciplinary Optimization

    12 publications · 522 citations

    12

    h-index

  2. Applied Soft Computing

    8 publications · 360 citations

    8

    h-index

  3. IEEE Transactions on Magnetics

    8 publications · 422 citations

    8

    h-index

  4. Information Sciences

    4 publications · 270 citations

    4

    h-index

  5. Journal of Hydrology

    4 publications · 229 citations

    4

    h-index

1–5