Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

Xiaoling Wang; Qi Kang; MengChu Zhou; Qi Deng; Zheng Fan; Haoyue Liu

doi:10.1109/JAS.2024.125070

IEEE/CAA Journal of Automatica Sinica

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X. Wang, Q. Kang, M. C. Zhou, Q. Deng, Z. Fan, and H. Liu, “Knowledge classification-assisted evolutionary multitasking for two-task multiobjective optimization problems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2024.125070

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X. Wang, Q. Kang, M. C. Zhou, Q. Deng, Z. Fan, and H. Liu, “Knowledge classification-assisted evolutionary multitasking for two-task multiobjective optimization problems,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2024.125070

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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

doi: 10.1109/JAS.2024.125070

Funds: This work was supported in part by the National Natural Science Foundation of China (51775385), the Natural Science Foundation of Shanghai (23ZR1466000), the Shanghai Industrial Collaborative Science and Technology Innovation Project (2021-cyxt2-kj10), and the Innovation Program of Shanghai Municipal Education Commission (202101070007E00098)

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Author Bio:
Xiaoling Wang received the M.S. degree from Central South University, in 2018. She is currently pursuing the Ph.D. degree in control theory and control engineering with Department of Control Science and Engineering, Tongji University. Her research interests include evolutionary computation, multitask optimization, and multiobjective optimization

Qi Kang (Senior Member, IEEE) received the B.S degree in automatic control and the M.S. and Ph.D. degrees in control theory and control engineering from Tongji University, in 2002, 2005 and 2009, respectively. He is currently a Professor with the Department of Control Science and Engineering, Tongji University. His research interests include evolutionary computation, machine learning, intelligent control and optimization. Dr. Kang is an Associate Editor for IEEE Transactions on Intelligent Transportation Systems. He is the Secretary General with Shanghai Association for System Simulation

Mengchu Zhou (Fellow, IEEE) received his B.S. degree from Nanjing University of Science and Technology, China in 1983, M.S. degree from Beijing Institute of Technology, China in 1986, and Ph. D. degree from Rensselaer Polytechnic Institute, Troy, NY in 1990. He then joined New Jersey Institute of Technology and is now a Distinguished Professor. His interests are in Petri nets, intelligent automation, Internet of Things, and big data analytics. He has over 1100 publications including 14 books, 750+ journal papers (600+ in IEEE transactions), 31 patents and 32 book-chapters. He is Fellow of IFAC, AAAS, CAA and NAI

Qi Deng received the B.S. degree in automation from Tongji University, in 2020. He is currently pursuing the Ph.D. degree in control science and engineering at Tongji University. His current research interests include evolutionary computation and multi-objective optimization

Zheng Fan received the M.S. degree in transportation planning and management from Civil Aviation Flight University of China, in 2015. He is currently pursuing the Ph.D. degree in control science and engineering, Tongji University. His current research interests include multi-objective optimization, intelligent control, and intelligent algorithms

HaoYue Liu (Member, IEEE) received the Ph.D. degree from New Jersey Institute of Technology, USA, in 2022. She is currently a Lecturer with the School of Information and Electronic Engineering, Zhejiang Gongshang University. Her research interests include imbalanced data analysis, natural language processing, sentiment analysis, and cyberbullying detection
Corresponding author: MengChu Zhou, e-mail: zhou@njit.edu
Received Date: 2024-10-17
Revised Date: 2024-11-18
Accepted Date: 2024-11-23

Available Online: 2025-04-10

Abstract

Abstract

To realize Industry 5.0, manufacturers face various optimization problems that seldom appear in isolation. Evolutionary MultiTasking (EMT) is an effective method to solve multiple related problems by extracting and utilizing common knowledge. Knowledge transfer is the key to the effectiveness of EMT. Existing EMT methods mainly focus on designing effective inter-task learning methods and ignore the fact that provided knowledge’s appropriateness also has a significant effect on EMT’s performance. There is plentiful knowledge in assistant tasks, and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks. EMT is thus confronted with a challenge to find appropriate knowledge. This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks. During the evolution process, better-performing candidates are supposed to have advantages in exploitation. Therefore, assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge. Specifically, the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population. Considering that target and assistant sub-populations have different characteristics, we use domain adaptation to reduce their distribution discrepancies. In this way, the trained classifier can classify assistant individuals more accurately, and truly useful knowledge can be selected for target tasks. The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.
- Artificial intelligence,
- evolutionary multitasking,
- intelligent optimization,
- knowledge classification,
- knowledge transfer,
- inter-task learning

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