A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918083623387136 |
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| author | Farias, Lucas R. C. Santos, Abimael J. F. Nobre, Matheus R. B. |
| author_facet | Farias, Lucas R. C. Santos, Abimael J. F. Nobre, Matheus R. B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Non-Dominated Sorting Evolutionary Algorithm Updating When Required Farias, Lucas R. C. Santos, Abimael J. F. Nobre, Matheus R. B. Neural and Evolutionary Computing 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. |
| title | A Non-Dominated Sorting Evolutionary Algorithm Updating When Required |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2507.03864 |