TY - JOUR
T1 - A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems
AU - Cui, Meiji
AU - Li, Li
AU - Zhou, Mengchu
AU - Li, Jiankai
AU - Abusorrah, Abdullah
AU - Sedraoui, Khaled
N1 - Funding Information:
We appreciate the help of Chia-Mei Jen-Wang for building the flow reactor and of Jan Beane of the Iowa State University Mass Spectrometry laboratory for performing the deuterium analyses. We also thank Dr. Mark Ekman and Felix Koo for synthesizing the catalyst. This work was conducted at the Ames Laboratory, which is operated for the US Department of Energy by Iowa State University under Contract No. W-7405Eng-82. This research was supported by the Office of Basic Energy Sciences, Chemical Sciences Division.
Publisher Copyright:
© 2014 Chinese Association of Automation.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - Autoencoder
KW - dimension reduction
KW - evolutionary algorithm
KW - medium-scale expensive problems
KW - teaching-learning-based optimization
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U2 - 10.1109/JAS.2022.105425
DO - 10.1109/JAS.2022.105425
M3 - Article
AN - SCOPUS:85128313173
SN - 2329-9266
VL - 9
SP - 1952
EP - 1966
JO - IEEE/CAA Journal of Automatica Sinica
JF - IEEE/CAA Journal of Automatica Sinica
IS - 11
ER -