{"ID":2898025,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03864","arxiv_id":"2507.03864","title":"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.","short_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-I...","url_abs":"https://arxiv.org/abs/2507.03864","url_pdf":"https://arxiv.org/pdf/2507.03864v1","authors":"[\"Lucas R. C. Farias\",\"Abimael J. F. Santos\",\"Matheus R. B. Nobre\"]","published":"2025-07-05T02:28:10Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false}