Real-time data-driven automatic design of multi-objective evolutionary algorithm: A case study on production scheduling
Multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi-objective optimization problems. The performance of MOEAs is highly dependent on the algorithm configuration. Therefore, the algorithm configuration is an essential task in the development and applicati...
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| Veröffentlicht in: | Applied soft computing Jg. 138; S. 110187 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.05.2023
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | Multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi-objective optimization problems. The performance of MOEAs is highly dependent on the algorithm configuration. Therefore, the algorithm configuration is an essential task in the development and application of MOEAs. In this paper, a real-time data-driven automatic design method for configuring an MOEA with minimal user interference is developed. Real-time data are driven in two ways. One lies in that the learning model is constructed based on the elite configurations selected by the Iterated F-Race (I/F-Race), which is used to bias the sampling toward the best configurations. Another is that the decision tree model is constructed by collecting the evaluated configurations in the process of I/F-Race as the data, and used to help identify the potential configurations to improve the sampling quality. In addition, a configurable MOEA (CMOEA) framework is summarized by integrating three general fitness assignment methods. In the experimental study, a case study on a multi-objective hybrid flowshop scheduling problem is conducted. By comparing with other variants of I/F-Race, the developed method is verified to have the ability of evaluating the promising configurations more fully and conceiving the best MOEA. Compared with the famous frameworks and state-of-the-art MOEAs, the proposed CMOEA framework and the automated algorithm show their superiorities based on different performance metrics.
•A CART-enhanced I/F-Race is developed.•A configurable MOEA framework is developed.•A case study on a multi-objective HFSP is studied. |
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| AbstractList | Multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi-objective optimization problems. The performance of MOEAs is highly dependent on the algorithm configuration. Therefore, the algorithm configuration is an essential task in the development and application of MOEAs. In this paper, a real-time data-driven automatic design method for configuring an MOEA with minimal user interference is developed. Real-time data are driven in two ways. One lies in that the learning model is constructed based on the elite configurations selected by the Iterated F-Race (I/F-Race), which is used to bias the sampling toward the best configurations. Another is that the decision tree model is constructed by collecting the evaluated configurations in the process of I/F-Race as the data, and used to help identify the potential configurations to improve the sampling quality. In addition, a configurable MOEA (CMOEA) framework is summarized by integrating three general fitness assignment methods. In the experimental study, a case study on a multi-objective hybrid flowshop scheduling problem is conducted. By comparing with other variants of I/F-Race, the developed method is verified to have the ability of evaluating the promising configurations more fully and conceiving the best MOEA. Compared with the famous frameworks and state-of-the-art MOEAs, the proposed CMOEA framework and the automated algorithm show their superiorities based on different performance metrics.
•A CART-enhanced I/F-Race is developed.•A configurable MOEA framework is developed.•A case study on a multi-objective HFSP is studied. |
| ArticleNumber | 110187 |
| Author | Zhang, Biao Lu, Chao Meng, Lei-lei Li, Jun-qing |
| Author_xml | – sequence: 1 givenname: Biao surname: Zhang fullname: Zhang, Biao organization: School of Computer Science, Liaocheng University, Liaocheng, 25200, PR China – sequence: 2 givenname: Lei-lei orcidid: 0000-0003-1439-4832 surname: Meng fullname: Meng, Lei-lei organization: School of Computer Science, Liaocheng University, Liaocheng, 25200, PR China – sequence: 3 givenname: Chao orcidid: 0000-0003-4637-6065 surname: Lu fullname: Lu, Chao email: luchao@cug.edu.cn organization: School of Computer Science, China University of Geosciences, Wuhan, 430074, PR China – sequence: 4 givenname: Jun-qing surname: Li fullname: Li, Jun-qing organization: School of Computer Science, Shandong Normal University, Jinan, 252000, PR China |
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| Cites_doi | 10.1016/j.cie.2021.107337 10.1007/s10479-009-0657-6 10.1109/TEVC.2007.892759 10.1109/TSMC.2019.2916088 10.1016/j.swevo.2022.101084 10.1016/j.knosys.2021.107819 10.1016/j.asoc.2014.11.006 10.1016/j.eswa.2017.10.038 10.1007/s00500-012-0946-x 10.1016/j.knosys.2022.108732 10.1007/s12065-020-00487-5 10.1016/j.swevo.2022.101079 10.1109/TII.2020.3043734 10.1016/j.ejor.2020.09.022 10.1016/j.ejor.2019.10.004 10.1109/TEVC.2019.2896002 10.1162/evco_a_00263 10.1109/4235.996017 10.1109/TCYB.2020.3041494 10.1007/978-3-642-02538-9_13 10.1016/j.cie.2019.07.036 10.1080/00207543.2021.1925772 10.1016/j.cie.2021.107835 10.1109/TEVC.2015.2474158 10.1016/j.asoc.2021.107305 10.1109/TEVC.2019.2921598 10.1080/00207543.2020.1741716 10.1016/j.swevo.2022.101097 10.1109/TEVC.2018.2791283 10.1109/TITS.2019.2954982 |
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| Keywords | Hybrid flowshop scheduling problem I/F-race Multi-objective evolutionary algorithm Decision tree Automatic algorithm design |
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