A Two-Stage Dominance-Based Surrogate-Assisted Evolution Algorithm for High-Dimensional Expensive Multi-Objective Optimization
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| Title: | A Two-Stage Dominance-Based Surrogate-Assisted Evolution Algorithm for High-Dimensional Expensive Multi-Objective Optimization |
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| Authors: | Wanliang Wang, Mengjiao Yu, Rui Dai, Zhongkui Chen |
| Publisher Information: | Research Square Platform LLC, 2023. |
| Publication Year: | 2023 |
| Description: | 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 for high-dimensional expensive multi-objective optimization (TSDEA) 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. |
| Document Type: | Article |
| DOI: | 10.21203/rs.3.rs-2638614/v1 |
| Rights: | CC BY |
| Accession Number: | edsair.doi...........a6b3fb2a3441b2010aaa20c097eabd72 |
| Database: | OpenAIRE |
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