Indicator-based Multiobjective Optimization

Indicator-Based Multiobjective Optimization is a multiobjective optimization paradigm that employs performance indicators as the primary criterion for comparing and selecting solution sets, thereby guiding the search process by directly optimizing the quality of the Pareto approximation with respect to a specific indicator.

93

Publications

7.5K

Citations

256

Authors

152

Institutions

Publications per year

2017–2026

55

Authors

256

  1. HI
    Hisao Ishibuchi

    Osaka Prefecture University, Japan

    9 publications · 1.1K citations

    9

    h-index

  2. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    5 publications · 904 citations

    5

    h-index

  3. FL
    Fei Li

    Northeastern University, China

    4 publications · 162 citations

    4

    h-index

  4. CA
    Carlos A. Coello Coello

    Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico

    4 publications · 216 citations

    4

    h-index

  5. XY
    Xin Yao

    University of Birmingham, United Kingdom

    4 publications · 521 citations

    4

    h-index

1–5 of 256

Venues

  1. IEEE Transactions on Evolutionary Computation

    12 publications · 3K citations

    12

    h-index

  2. IEEE Transactions on Cybernetics

    6 publications · 840 citations

    6

    h-index

  3. Information Sciences

    5 publications · 188 citations

    5

    h-index

  4. Applied Soft Computing

    4 publications · 177 citations

    4

    h-index

  5. Swarm and Evolutionary Computation

    3 publications · 163 citations

    3

    h-index

1–5