A Non-Dominated Sorting Evolutionary Algorithm Updating When Required

• Lucas R. C. Farias UFPE / CESAR School /UNICAP
• Abimael J. F. Santos UFPE
• Matheus R. B. Nobre UFPE

DOI: https://doi.org/10.5753/eniac.2025.14037

Resumo

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.

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