Skip to content
New issue

Have a question about this project? # for a free GitHub account to open an issue and contact its maintainers and the community.

#

By clicking “#”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? # to your account

Introduce normalization of F in NSGA3 extreme point calculation #557

Closed
wants to merge 3 commits into from
Closed

Introduce normalization of F in NSGA3 extreme point calculation #557

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0f45e3a
resolve bug that prevents two equally seeded reference direction calc…
Feb 2, 2024
18e38d8
normalize F before calculating extreme points
Feb 2, 2024
7b7cdf6
Revert "resolve bug that prevents two equally seeded reference direct…
Feb 2, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 4 additions & 1 deletion pymoo/algorithms/moo/nsga3.py
View file
Original file line number Diff line number Diff line change
@@ -312,8 +312,11 @@ def get_extreme_points_c(F, ideal_point, extreme_points=None):
__F = _F - ideal_point
__F[__F < 1e-3] = 0

# normalize __F
__F_norm = (__F - np.min(__F, axis=0)) / (np.max(__F, axis=0) - np.min(__F, axis=0))

# update the extreme points for the normalization having the highest asf value each
F_asf = np.max(__F * weights[:, None, :], axis=2)
F_asf = np.max(__F_norm * weights[:, None, :], axis=2)

I = np.argmin(F_asf, axis=1)
extreme_points = _F[I, :]