Research Article
A Knee Point-Driven Many-Objective Evolutionary Algorithm with Adaptive Switching Mechanism
Algorithm 3
Finding_knee_point (
, , , and
).Input:F (sorted population), T (rate of knee points in population), r, t (adaptive parameters) | 1 K ← ∅ / knee points / | 2 For allFi ∈ Fdo | 3 E ← Find_extreme_solution(Fi) | 4 L ← Calculate_extreme_hyperplane(E) | 5 Update r by Eq. (9) | 6 fmax ← Maximum value of each objective in Fi | 7 fmin ← Minimum value of each objective in Fi | 8 Calculate V by Eq. (8) | 9 Calculate the distance from each solution in Fi to L by Eq. (7) | 10 Sort Fi in a descending order according to the distances | 11 SizeFi← | 12 For allp ∈ Fido | 13 | 14 K ← K ⋃ {p} | 15 Fi ← Fi∖NB | 16 End For | 17 | 18 End for | 19 ReturnK, r and t |
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