A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
Abstract
The NSGA-III algorithm relies on uniformly distributed reference points to promote diversity in many-objective optimization problems. However, this strategy may underperform when facing irregular Pareto fronts, where certain vectors remain unassociated with any optimal solutions. While adaptive schemes such as A-NSGA-III address this issue by dynamically modifying reference points, they may introduce unnecessary complexity in regular scenarios. This paper proposes NSGA-III with Update when Required (NSGA-III-UR), a hybrid algorithm that selectively activates reference vector adaptation based on the estimated regularity of the Pareto front. Experimental results on benchmark suites (DTLZ1-7, IDTLZ1-2) and real-world problems demonstrate that NSGA-III-UR consistently outperforms NSGA-III and A-NSGA-III across diverse problem landscapes.
Keywords
Cite
@article{arxiv.2507.03864,
title = {A Non-Dominated Sorting Evolutionary Algorithm Updating When Required},
author = {Lucas R. C. Farias and Abimael J. F. Santos and Matheus R. B. Nobre},
journal= {arXiv preprint arXiv:2507.03864},
year = {2025}
}
Comments
12 pages, 3 figures, under review for ENIAC 2025