Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Shouyong Jiang; Jinglei Guo; Yong Wang; Shengxiang Yang

doi:10.1109/JAS.2024.124515

Volume 11 Issue 9

Sep. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(9): 1973-1986

S. Jiang, J. Guo, Y. Wang, and S. Yang, “Evolutionary multi/many-objective optimisation via bilevel decomposition,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1973–1986, Sept. 2024. doi: 10.1109/JAS.2024.124515

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S. Jiang, J. Guo, Y. Wang, and S. Yang, “Evolutionary multi/many-objective optimisation via bilevel decomposition,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1973–1986, Sept. 2024. doi: 10.1109/JAS.2024.124515

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Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

doi: 10.1109/JAS.2024.124515

Funds: This work was supported in part by the National Natural Science Foundation of China (62376288, U23A20347), the Engineering and Physical Sciences Research Council of UK (EP/X041239/1), and the Royal Society International Exchanges Scheme of UK (IEC/NSFC/211404)

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Author Bio:
Shouyong Jiang (Member, IEEE) received the B.Sc. degree in information and computation science and the M.Sc. degree in control theory and control engineering from Northeastern University in 2011 and 2013, respectively, and the Ph.D. degree in computer science from De Montfort University UK in 2017. He is a Professor with the School of Automation, Central South University. His current research interests include computational intelligence, artificial intelligence and relevant interdisciplinary research

Jinglei Guo received the B.S. and M.S. degrees in computer science from Wuhan University of Technology in 1999 and 2002 respectively, the Ph.D. degree in computer science from Wuhan University in 2011. She is currently an Associate Professor with the School of Computer Science, Central China Normal University. Her research interest includes intelligent computing, large-scale global optimization, and parallel computing. She has published more than 20 international journal/conference papers. She serves as a Reviewer for more than ten international journals

Yong Wang (Senior Member, IEEE) received the Ph.D. degree in control science and engineering from the Central South University in 2011. He is a Professor with the School of Automation, Central South University. His current research interests include intelligent learning and optimization and their interdisciplinary applications.Dr. Wang was a recipient of Cheung Kong Young Scholar by the Ministry of Education, China in 2018, a Web of Science Highly Cited Researcher in computer science in 2017 and 2018, and a Elsevier’s Most Cited Chinese Researcher in 2021–2023. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the Swarm and Evolutionary Computation

Shengxiang Yang (Senior Member, IEEE) received the Ph.D. degree in systems engineering from Northeastern University in 1999. He is currently a Professor of computational intelligence and the Deputy Director of the Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, UK. His current research interests include evolutionary computation, swarm intelligence, artificial neural networks, data mining and data stream mining, and relevant real-world applications.Prof. Yang has over 440 publications with an H-index of 74 according to Google Scholar. He serves as an Associate Editor/Editorial Board Member for a number of international journals, such as the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, Enterprise Information Systems, and CAAI Transactions on Intelligence Technology
Corresponding author: Shouyong Jiang, e-mail: sjiang@csu.edu.cn; Yong Wang, e-mail: ywang@csu.edu.cn
¹ Supplementary Material of this paper can be found in link https://github.com/chang88ye/M2M-BL.
Received Date: 2024-01-29
Revised Date: 2024-03-26
Accepted Date: 2024-04-07

Available Online: 2024-07-04

Abstract

Abstract

Decomposition of a complex multi-objective optimisation problem (MOP) to multiple simple subMOPs, known as M2M for short, is an effective approach to multi-objective optimisation. However, M2M facilitates little communication/collaboration between subMOPs, which limits its use in complex optimisation scenarios. This paper extends the M2M framework to develop a unified algorithm for both multi-objective and many-objective optimisation. Through bilevel decomposition, an MOP is divided into multiple subMOPs at upper level, each of which is further divided into a number of single-objective subproblems at lower level. Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another, and eventually to all the subMOPs. The bilevel decomposition is readily combined with some new mating selection and population update strategies, leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multi- and many-objective optimisation. Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.
- Bilevel decomposition,
- evolutionary algorithm,
- many-objective optimisation,
- multi-objective optimisation

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¹ Supplementary Material of this paper can be found in link https://github.com/chang88ye/M2M-BL.

References(51)

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Bilevel decomposition of problems into upper-level leaders and lower-level followers
Active interaction between leaders and followers for fast information propagation
New mating selection to balance local exploitation and global exploration
New solution replacement to enhance the quality of replacement and usage of offspring

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

doi: 10.1109/JAS.2024.124515

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