# A Non-Dominated Sorting Evolutionary Algorithm Updating When Required

arXiv: https://arxiv.org/abs/2507.03864 · HTML: https://mlsanity.com/paper/2507.03864/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Morephy-Net: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Neural Operator Learning Networks](https://mlsanity.com/paper/2509.00663/) (2025)

## Abstract (arXiv)

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.