Decomposition-based Optimization

Decomposition-Based Optimization is a multi-objective optimization approach that decomposes a complex problem into multiple simpler subproblems through scalarization techniques, enabling the simultaneous optimization of conflicting objectives by coordinating solutions across subproblems.

999

Publications

58.3K

Citations

2.4K

Authors

855

Institutions

Publications per year

2017–2026

363

Authors

2.4K

  1. QZ
    Qingfu Zhang

    City University of Hong Kong, Hong Kong SAR China

    24 publications · 4.4K citations

    22

    h-index

  2. XY
    Xin Yao

    University of Birmingham, United Kingdom

    17 publications · 2.9K citations

    16

    h-index

  3. PY
    Panos Y. Papalambros

    University of Michigan, United States

    19 publications · 615 citations

    15

    h-index

  4. XL
    Xiaodong Li

    RMIT University, Australia

    13 publications · 2.2K citations

    13

    h-index

  5. CA
    Carlos A. Coello Coello

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

    14 publications · 629 citations

    13

    h-index

1–5 of 2.4K

Venues

  1. IEEE Transactions on Evolutionary Computation

    42 publications · 7.7K citations

    33

    h-index

  2. IEEE Transactions on Cybernetics

    27 publications · 2.3K citations

    24

    h-index

  3. IEEE Transactions on Power Systems

    23 publications · 1.7K citations

    23

    h-index

  4. Information Sciences

    19 publications · 703 citations

    17

    h-index

  5. Computers & Chemical Engineering

    20 publications · 849 citations

    16

    h-index

1–5