This a MATLAB implementation of NSGA-III. Jan and Deb, extended the well-know NSGA-II to deal with many-objective optimization problem, using a reference point approach, with non-dominated sorting mechanism. The newly developed algorithm is simply called: NSGA-III. The main reference paper is available here: http://doi.org/10.1109/TEVC.2013.2281535.
For more information, see following link:
Yarpiz (2020). NSGA-III in MATLAB (https://www.mathworks.com/matlabcentral/fileexchange/60678-nsga-iii-in-matlab), MATLAB Central File Exchange. Retrieved .
I am sorry to find that I cannot obtain a pareto surface by this algorithm. I found the reference points Zr is not used at all.
Can this arithmetic solve constrainted MOPs?Thank you!
please inform how to change objective function in the code, i changed mop function but it gives error for 4 objective functions
Why wasn't simulated binary crossover (SBX) used. It was used in the actual paper but not in this code.
Sir, Can u please provide the same code for image clustering.
this code is not correct
this matlab nsga3 code normalizes entire population but according to the reference article, we must normalizes St population
( page 5 and 6 article :An Evolutionary Many-Objective Optimization
Algorithm Using Reference-point Based
Non-dominated Sorting Approach,
Part I: Solving Problems with Box Constraints)
at the code ( [pop, params] = NormalizePopulation(pop, params); ) it s the part that u must check
this is one of problem of this code
Can this arithmetic solve constrainted problems?Thank you!
Thank you, I just need it.
Inspired by: Non-dominated Sorting Genetic Algorithm II (NSGA-II)