Skip to content
New issue

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

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

Already on GitHub? Sign in to your account

Introduce normalization of F in NSGA3 extreme point calculation #557

Open
wants to merge 3 commits into
base: main
Choose a base branch
from

Conversation

NikHoh
Copy link

@NikHoh NikHoh commented Feb 2, 2024

As the extreme point calculation is happening as a weighted and aggregated optimization (max(__F * weights)), I think it would be better to normalize __F beforehand. Otherwise, the resulting extreme points could have a bias towards objective functions (F) with higher magnitude (in case the objective values of different objective functions are highly unbalanced).

Hohmann, Nikolas added 3 commits February 2, 2024 16:26
…ulations (energy reduction method) to produce the same output
…ion calculations (energy reduction method) to produce the same output"

This reverts commit 0f45e3a.

revert commit
@blankjul blankjul self-assigned this Jul 7, 2024
@blankjul
Copy link
Collaborator

blankjul commented Jul 7, 2024

Thanks for your work and PR. Have you benchmark this change?
The NSGA3 implementation follows the original C++ code of the paper. Thus, I want to keep it as it is by default.
However, if this performs better and is benchmark, introducing this as a parameter might be a good idea.

Can you post some benchmark results here with this change?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants