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

Volume 12 Issue 6

Jun. 2025

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > 12(6): 1176-1193

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, vol. 12, no. 6, pp. 1176–1193, Jun. 2025. doi: 10.1109/JAS.2024.125070

Citation:

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, vol. 12, no. 6, pp. 1176–1193, Jun. 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), the Innovation Program of Shanghai Municipal Education Commission (202101070007E00098), and Tongxiang Institute of Artificial General Intelligence (TAGI2-A-2024-0006)

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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. From 2007 to 2008, he was a Research Associate with the University of Illinois at Chicago, USA. From 2014 to 2015, he was a Visiting Scholar with the New Jersey Institute of Technology, USA. He is currently a Professor with the Department of Control Science and Engineering and the Shanghai Institute of Intelligent Science and Technology, Tongji University. His interests are in swarm intelligence, evolutionary computation, machine learning, and intelligent control and optimization. Dr. Kang is currently served as the General Chair of the 19th IEEE International Conference on Networking, Sensing and Control (ICNSC 2022), 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 in electrical and computer engineering. His research interests are intelligent automation/transportation, robotics, Petri nets, Internet of Things, edge/cloud computing, and big data analytics. He has over 1300 publications including 17 books, over 900 journal papers including over 700 IEEE Transactions papers, 31 patents and 32 book-chapters. His recently co-authored books include Learning Automata and their Applications to Intelligent Systems, IEEE Press/Wiley, Hoboken, NJ, 2024 (with J. Zhang), Device-Edge-Cloud Continuum Paradigms, Architectures and Applications, Springer Nature, 2023 (with C. Savaglio, G. Fortino, and J. Ma) and Sustainable Manufacturing Systems: An Energy Perspective, IEEE Press/Wiley, Hoboken, Hoboken, NJ, 2022 (with L. Li). He is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man, and Cybernetics Society, and Edison Patent Award from the Research & Development Council of New Jersey. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA) and National Academy of Inventors (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. From 2022 to 2023, she was a Visiting Assistant Professor with the University of Northern Illinois University, USA. 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; Qi Kang, e-mail: qkang@tongji.edu.cn
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,
- inter-task learning,
- knowledge classification,
- knowledge transfer,
- machine learning,
- multiobjective optimization

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