About me
Li Wei

Li Wei, a doctor of engineering, professor, and doctoral supervisor, currently serves as the vice dean of the School of Information Engineering. He previously served as a visiting vice dean at the School of Information Science and Technology, University of Science and Technology of China. He is a communication review expert for the National Natural Science Foundation of China, a review expert for science and technology projects in Jiangxi Province, a review expert for the identification of high-tech enterprises in Jiangxi Province, an expert at the Ganzhou Digital Economy Research Institute, a consultant for the development of the digital economy in Nankang District, Ganzhou City, an excellent instructor for national and provincial artificial intelligence-related discipline competitions, and has been recognized as one of the "Top Ten Most Popular Teachers" by the university, as well as an excellent teacher, a young science and technology pacesetter, an excellent graduate supervisor, and an excellent dissertation supervisor. He is a visiting scholar at the University of Calgary in Canada and currently serves as a member of the IEEE Computational Intelligence Society, a member of the Chinese Computer Society, an executive director of the Intelligent Simulation Optimization and Scheduling Specialty Committee of the Chinese Simulation Society, a deputy director of the Intelligent Computing Specialty Committee of the Jiangxi Computer Society, and a council member of the Jiangxi Artificial Intelligence Society. He primarily engages in interdisciplinary research in artificial intelligence, intelligent optimization algorithms, AI+ materials science and engineering, and AI+ metallurgical engineering. He has led and completed multiple national natural science foundation and provincial and ministerial-level scientific research projects, and participated in over 20 national, provincial, and ministerial-level scientific research projects. As the first author and corresponding author, he has published over 50 high-level papers in domestic and international high-level journals, several of which are ranked in the top journals of the first quartile by the Chinese Academy of Sciences and are considered high-quality and excellent domestic journals. The Computational Intelligence Laboratory he established has project collaborations with multiple top intelligent computing laboratories both domestically and internationally, as well as in Hong Kong, Macao, and Taiwan.

Contact information: liwei@jxust.edu.cn.

News
22/Sep./2025: Our paper entitled: " Reinforcement Knowledge Sharing Assisted Two-Archive Evolutionary Algorithm for Many-Objective Optimization " was accepted by Swarm and Evolutionary Computation.
22/Sep./2025: Our paper entitled: " A Multi-Distance Co-Selection Evolutionary Algorithm for Many-Objective Optimization " was accepted by Expert Systems with Applications
14/Jun./2025: Our paper entitled: " A solution potential-based adaptation reference vector evolutionary algorithm for many-objective optimization " was accepted by Swarm and Evolutionary Computation.
14/Jun./2025: Our paper entitled: " An Information Entropy-Driven Evolutionary Algorithm Based on Reinforcement Learning for Many-Objective Optimization " was accepted by Expert Systems with Applications
14/Jun./2025: Our paper entitled: " Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy " was accepted by Swarm and Evolutionary Computation
14/Jun./2025: Our paper entitled: " DC-SHADE-IF:An infeasible-feasible regions constrained optimization approach with diversity controller " was accepted by Expert Systems with Applications.
Journal papers
Wei Li, Zhiting Liu, Ning Yang, Qing Xu, Ying Huang, Weize Qin. A multi-distance co-selection evolutionary algorithm for many-objective optimization. Expert Systems with Applications,Volume 297,2026,129534. (JCR Q1)
Qing Xu, Shuzheng Xie, Ning Yang, Ying Huang, Shaochang Nie, Wei Li. Reinforcement knowledge sharing assisted two-archive evolutionary algorithm for many-objective optimization. Swarm and Evolutionary Computation,Volume 98,2025,102139. (JCR Q1)
Peng Liang, Yangtao Chen, Yafeng Sun, Ying Huang, Wei Li. An information entropy-driven evolutionary algorithm based on reinforcement learning for many-objective optimization. Expert Systems with Applications,Volume 238,2024,122164. (JCR Q1)
Wei Li, Yangtao Chen, Yuehua Dong, Ying Huang. A solution potential-based adaptation reference vector evolutionary algorithm for many-objective optimization. Swarm and Evolutionary Computation, Volume 84, 2024, 101451. (JCR Q1)
Wei Li, Peng Liang, Bo Sun, Yafeng Sun, Ying Huang. Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy. Swarm and Evolutionary Computation, Volume 78, 2023, 101274. (JCR Q1)
Wei Li, Bo Sun, Yafeng Sun, Ying Huang, Yiu-ming Cheung, Fangqing Gu. DC-SHADE-IF: An infeasible–feasible regions constrained optimization approach with diversity controller,, Expert Systems with Applications, Volume 224,2023,119999. (JCR Q1)
Yuelin Qu, Yuhang Hu, Wei Li, Ying Huang. Promising boundaries explore and resource allocation evolutionary algorithm for constrained multiobjective optimization. Swarm and Evolutionary Computation Volume 92,2025,101819. (JCR Q1)
Wei Li, Xiaolong Zeng, Ying Huang, Yiu-ming Cheung. HK-MOEA/D: A historical knowledge-guided resource allocation for decomposition multiobjective optimization. Engineering Applications of Artificial Intelligence Volume 139,Part A,2025,109482. (JCR Q1)
Yang, Ning and Liu, Hai-Lin. Bilevel Evolutionary Multi-objective Algorithm with Multiple Lower-level Search Modes IEEE Transactions on Evolutionary Computation , volume.30,1,296-310. (JCR Q1)
W. Li, Y. Chen, Q. Cai, C. Wang, Y. Huang and S. Mahmoodi. Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems. Complex System Modeling and Simulation,volume.2,4,288-306 (JCR Q1)
Wei Li, Yangtao Chen, Yuehua Dong, Ying Huang. A solution potential-based adaptation reference vector evolutionary algorithm for many-objective optimizatio. Swarm and Evolutionary Computation,Volume 84,2024,101451. (JCR Q1)
W. Li, X. Yan and Y. Huang. Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem Tsinghua Science and Technology, volumn.29,5,1283-1299. (JCR Q2)
Wei Li, Xiang Meng, Ying Huang, Zhang-Hua Fu Multipopulation cooperative particle swarm optimization with a mixed mutation strategy,Information Sciences. Information Sciences,Volume 529,2020,179-196 (JCR Q2)
Wei Li, Cancan Wang, Ying Huang, Yiu-ming Cheung. Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem. Applied Soft Computing,Volume 133,2023,109943 (JCR Q1)
Wei Li, Xiang Meng, Ying Huang. Fitness distance correlation and mixed search strategy for differential evolution. Neurocomputing,Volume 458,2021,Pages 514-525. (JCR Q1)
Li, W., Li, S., Chen, Z Self-feedback differential evolution adapting to fitness landscape characteristics. Soft Comput 23,1151–1163. (JCR Q3)
Wei Li, Jianghui Jing, Yangtao Chen, Yishan Chen A cooperative particle swarm optimization with difference learning. Information Sciences,Volume 643,2023,119238 (JCR Q2)
Li, W., Sun, YF.,Huang, Y. An adaptive differential evolution algorithm using fitness distance correlation and neighbourhood-based mutation strategy. CONNECTION SCIENCE (JCR Q2)
Ying Huang, Ling Lai, Wei Li, Hui Wang. A differential evolution algorithm with ternary search tree for solving the three-dimensional packing problem Information Sciences,Volume 606,2022,440-452. (JCR Q2)
Ying Huang, Wei Li, Furong Tian, Xiang Meng. A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy. Applied Soft Computing,Volume 96,2020,106693. (JCR Q1)