Pareto-based Optimization

Pareto-Based Optimization is a multi-objective optimization approach that identifies solutions which are not dominated by any other solution in terms of all objectives, thereby forming a Pareto-optimal set where trade-offs between conflicting objectives are explicitly preserved.

968

Publications

52.6K

Citations

2.5K

Authors

954

Institutions

Publications per year

2017–2026

325

Authors

2.5K

  1. AM
    Achille Messac

    Rensselaer Polytechnic Institute, United States

    14 publications · 1.4K citations

    13

    h-index

  2. JW
    Johann W. Kolar

    ETH Zurich, Switzerland

    11 publications · 977 citations

    11

    h-index

  3. KD
    Kalyanmoy Deb

    Michigan State University, United States

    10 publications · 1.2K citations

    10

    h-index

  4. CA
    Christopher A. Mattson

    Brigham Young University, United States

    9 publications · 429 citations

    9

    h-index

  5. SY
    Shengxiang Yang

    De Montfort University, United Kingdom

    8 publications · 1.3K citations

    8

    h-index

1–5 of 2.5K

Venues

  1. IEEE Transactions on Evolutionary Computation

    22 publications · 4.1K citations

    20

    h-index

  2. Structural and Multidisciplinary Optimization

    16 publications · 3.2K citations

    15

    h-index

  3. Applied Soft Computing

    17 publications · 823 citations

    14

    h-index

  4. Engineering Optimization

    19 publications · 1.4K citations

    14

    h-index

  5. IEEE Transactions on Cybernetics

    12 publications · 840 citations

    12

    h-index

1–5