A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems
文献类型：期刊论文
| 作者 | Meiji Cui; Li Li; MengChu Zhou; Jiankai Li; Abdullah Abusorrah; Khaled Sedraoui |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2022 |
| 卷号 | 9期号:11页码:1952-1966 |
| ISSN号 | 2329-9266 |
| 关键词 | Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization |
| DOI | 10.1109/JAS.2022.105425 |
| 英文摘要 | This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49926]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Meiji Cui,Li Li,MengChu Zhou,et al. A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(11):1952-1966. |
| APA | Meiji Cui,Li Li,MengChu Zhou,Jiankai Li,Abdullah Abusorrah,&Khaled Sedraoui.(2022).A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems.IEEE/CAA Journal of Automatica Sinica,9(11),1952-1966. |
| MLA | Meiji Cui,et al."A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems".IEEE/CAA Journal of Automatica Sinica 9.11(2022):1952-1966. |
入库方式： OAI收割
来源：自动化研究所
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