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A two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization.
- Source :
- Scientific Reports; 8/13/2023, Vol. 13 Issue 1, p1-17, 17p
- Publication Year :
- 2023
-
Abstract
- In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required to build accurate surrogate models, which is unrealistic for high-dimensional EMOPs. Therefore, this paper develops a two-stage dominance-based surrogate-assisted evolution algorithm (TSDEA) for high-dimensional EMOPs which utilizes the RBF model to approximate each objective function. First, a two-stage selection strategy is applied to select individuals for re-evaluation. Then considering the training time of the model, proposing a novel archive updating strategy to limit the number of individuals for updating. Experimental results show that the proposed algorithm has promising performance and computational efficiency compared to the state-of-the-art five SAEAs. [ABSTRACT FROM AUTHOR]
- Subjects :
- EVOLUTIONARY algorithms
ALGORITHMS
SWARM intelligence
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 169911428
- Full Text :
- https://doi.org/10.1038/s41598-023-40019-6