Multiobjective Surrogate Optimization

Multiobjective Surrogate Optimization is a computational optimization paradigm that employs surrogate models to approximate multiple conflicting objective functions, thereby reducing the computational burden of evaluating expensive black-box functions in multiobjective decision-making problems.

268

Publications

10.4K

Citations

881

Authors

361

Institutions

Publications per year

2017–2026

166

Authors

881

  1. IC
    Ivo Couckuyt

    Ghent University, Belgium

    6 publications · 146 citations

    6

    h-index

  2. TD
    Tom Dhaene

    Ghent University, Belgium

    6 publications · 146 citations

    6

    h-index

  3. KK
    Kwang‐Yong Kim

    Inha University, South Korea

    6 publications · 187 citations

    6

    h-index

  4. YY
    Yacov Y. Haimes

    University of Virginia, United States

    5 publications · 111 citations

    5

    h-index

  5. LG
    Liang Gao

    Huazhong University of Science and Technology, China

    5 publications · 193 citations

    5

    h-index

1–5 of 881

Venues

  1. Applied Soft Computing

    10 publications · 365 citations

    10

    h-index

  2. Structural and Multidisciplinary Optimization

    8 publications · 332 citations

    8

    h-index

  3. IEEE Transactions on Magnetics

    8 publications · 356 citations

    8

    h-index

  4. IEEE Transactions on Evolutionary Computation

    8 publications · 413 citations

    8

    h-index

  5. Water Resources Research

    8 publications · 403 citations

    8

    h-index

1–5