Multiobjective Evolutionary Algorithms

Multiobjective Evolutionary Algorithms is a computational optimization methodology that extends evolutionary algorithms by simultaneously addressing multiple conflicting objectives through population-based search and Pareto optimality principles.

4.9K

Publications

380.9K

Citations

9.4K

Authors

2.4K

Institutions

Publications per year

2017–2026

1.9K

Variants

Moeas

Authors

9.4K

  1. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    94 publications · 19.6K citations

    58

    h-index

  2. QZ
    Qingfu Zhang

    City University of Hong Kong, Hong Kong SAR China

    83 publications · 24.3K citations

    52

    h-index

  3. KD
    Kalyanmoy Deb

    Michigan State University, United States

    98 publications · 35.1K citations

    52

    h-index

  4. CA
    Carlos A. Coello Coello

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

    111 publications · 16.8K citations

    50

    h-index

  5. KC
    Kay Chen Tan

    National University of Singapore, Singapore

    81 publications · 9.9K citations

    48

    h-index

1–5 of 9.4K

Venues

  1. IEEE Transactions on Evolutionary Computation

    344 publications · 101.8K citations

    136

    h-index

  2. IEEE Transactions on Cybernetics

    151 publications · 18.1K citations

    77

    h-index

  3. Applied Soft Computing

    165 publications · 7.5K citations

    49

    h-index

  4. Information Sciences

    127 publications · 6.8K citations

    44

    h-index

  5. Expert Systems with Applications

    83 publications · 5.3K citations

    39

    h-index

1–5