wreszelewski / nsga2 Goto Github PK

Hello,
I am very interested in your code and I may use it during my work on the PHD thesis if you have no problem.
Actually I am new in this domain (NSGA 2 algorithm and related concepts) and I am trying to understand the code.
During my research about NSGA 2 algorithm I found that the crowding distance for individuals (other than the first and last one) is the difference of the objective value of two closet neighbors.
But in your code, you calculated the crowding distance of an individual by the difference of the crowding distance of two closet neighbors.
Can you explain please?

Thank you in advance for your time.

I believe the crowding distance calculation is different from the one in the NSGA-II(Deb, 2000) paper.

The offending line of code is here, and looks like:

front[index].crowding_distance = (front[index+1].crowding_distance - front[index-1].crowding_distance) / (self.problem.max_objectives[m] - self.problem.min_objectives[m])

However, in (Deb 2000), the crowding distance is calculated as follows:

The last line would correspond to the above code.
Is there a particular reasoning behind this? Thanks.

Hi, I know this cannot be called an issue but I really need your help.
I have a multiobjective problem that consists of minimizing 2 functions f1 and f2. each of the two function take a vector X as argument. X will be a list or an np.array ... how can I use this nsga2 algorithm to optimize those two functions.
Thank you!

This might not fit under issues, but I hope you can help me anyways. I am exploring the option of using this code for a project at my university. (First of, is this ok?)
If I am to use it, I need to implement test problem ZDT6. What I am wondering is how you are able to calculate the perfect pareto front for problems 1, 2 and 3? How would I then do this for ZDT6?
I also have a bit of trouble understanding the hypervolume metric. Firstly, why do you choose 11, 11 as a reference point? Is this just random, and unimportant as long as the reference point is the same for all hypercubes? Lastly, how do you calculate the max hypervolume? Is this just the hypervolume for the pareto optimal set?