Knee Point Detection

Knee Point Detection is a solution selection methodology in multi-objective optimization that identifies the most preferred trade-off solution on the Pareto front where marginal improvements in one objective cease to justify losses in another, thereby distinguishing itself from general compromise-seeking techniques by focusing on high-impact transition points in objective trade-offs.

30

Publications

3K

Citations

112

Authors

50

Institutions

Publications per year

2017–2026

21

Authors

112

  1. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    5 publications · 208 citations

    5

    h-index

  2. MO
    Markus Olhofer

    Honda (Germany), Germany

    5 publications · 185 citations

    5

    h-index

  3. GY
    Guo Yu

    University of Surrey, United Kingdom

    4 publications · 163 citations

    4

    h-index

  4. PZ
    Ping Zhou

    Ansys (United States), United States

    1 publications · 151 citations

    1

    h-index

  5. PF
    Pasi Fränti

    University of Eastern Finland, Finland

    1 publications · 50 citations

    1

    h-index

1–5 of 112

Venues

  1. IEEE Transactions on Evolutionary Computation

    3 publications · 288 citations

    3

    h-index

  2. IEEE Transactions on Magnetics

    1 publications · 151 citations

    1

    h-index

  3. Mathematics

    1 publications · 27 citations

    1

    h-index

  4. Structural Health Monitoring

    SHM · 1 publications · 27 citations

    1

    h-index

  5. Soft Computing

    1 publications · 90 citations

    1

    h-index

1–5