Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems Q Lin, S Liu, Q Zhu, C Tang, R Song, J Chen, CAC Coello, KC Wong, ... IEEE Transactions on Evolutionary Computation 22 (1), 32-46, 2016 | 278 | 2016 |
A constrained multiobjective evolutionary algorithm with detect-and-escape strategy Q Zhu, Q Zhang, Q Lin IEEE Transactions on Evolutionary Computation 24 (5), 938-947, 2020 | 145 | 2020 |
A novel hybrid multi-objective immune algorithm with adaptive differential evolution Q Lin, Q Zhu, P Huang, J Chen, Z Ming, J Yu Computers & Operations Research 62, 95-111, 2015 | 130 | 2015 |
An external archive-guided multiobjective particle swarm optimization algorithm Q Zhu, Q Lin, W Chen, KC Wong, CAC Coello, J Li, J Chen, J Zhang IEEE transactions on cybernetics 47 (9), 2794-2808, 2017 | 126 | 2017 |
An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies Q Lin, Y Ma, J Chen, Q Zhu, CAC Coello, KC Wong, F Chen Information Sciences 430, 46-64, 2018 | 67 | 2018 |
A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm Q Zhu, Q Lin, Z Du, Z Liang, W Wang, Z Zhu, J Chen, P Huang, Z Ming Information Sciences 345, 177-198, 2016 | 62 | 2016 |
A multi-objective immune algorithm with dynamic population strategy Q Lin, Q Zhu, N Wang, P Huang, W Wang, J Chen, Z Ming Swarm and Evolutionary Computation 50, 100477, 2019 | 27 | 2019 |
An elite gene guided reproduction operator for many-objective optimization Q Zhu, Q Lin, J Li, CAC Coello, Z Ming, J Chen, J Zhang IEEE transactions on cybernetics 51 (2), 765-778, 2019 | 22 | 2019 |
A survey on evolutionary reinforcement learning algorithms Q Zhu, X Wu, Q Lin, L Ma, J Li, Z Ming, J Chen Neurocomputing 556, 126628, 2023 | 14 | 2023 |
Variable neighborhood search for a new practical dynamic pickup and delivery problem J Cai, Q Zhu, Q Lin Swarm and Evolutionary Computation 75, 101182, 2022 | 13 | 2022 |
A gene-level hybrid search framework for multiobjective evolutionary optimization Q Zhu, Q Lin, J Chen Neural Computing and Applications 30, 759-773, 2018 | 10 | 2018 |
MOEA/D with two types of weight vectors for handling constraints Q Zhu, Q Zhang, Q Lin, J Sun 2019 IEEE Congress on Evolutionary Computation (CEC), 1359-1365, 2019 | 8 | 2019 |
A Kriging model-based evolutionary algorithm with support vector machine for dynamic multimodal optimization X Wu, Q Lin, W Lin, Y Ye, Q Zhu, VCM Leung Engineering Applications of Artificial Intelligence 122, 106039, 2023 | 7 | 2023 |
A survey of dynamic pickup and delivery problems J Cai, Q Zhu, Q Lin, L Ma, J Li, Z Ming Neurocomputing, 126631, 2023 | 6 | 2023 |
An efficient multi-objective evolutionary algorithm for a practical dynamic pickup and delivery problem J Cai, Q Zhu, Q Lin, J Li, J Chen, Z Ming International conference on intelligent computing, 27-40, 2022 | 4 | 2022 |
A gene-level hybrid crossover operator for multiobjective evolutionary algorithm Q Zhu, Q Lin, J Chen, P Huang 2015 second international conference on soft computing and machine …, 2015 | 4 | 2015 |
An efficient evaluation mechanism for evolutionary reinforcement learning X Wu, Q Zhu, Q Lin, J Li, J Chen, Z Ming International Conference on Intelligent Computing, 41-50, 2022 | 2 | 2022 |
Evolutionary Reinforcement Learning with Action Sequence Search for Imperfect Information Games X Wu, Q Zhu, WN Chen, Q Lin, J Li, CAC Coello Information Sciences, 120804, 2024 | 1 | 2024 |
A Pareto dominance relation based on reference vectors for evolutionary many-objective optimization S Wang, H Wang, Z Wei, F Wang, Q Zhu, J Zhao, Z Cui Applied Soft Computing 157, 111505, 2024 | 1 | 2024 |
Two-Stage Evolutionary Reinforcement Learning for Enhancing Exploration and Exploitation Q Zhu, X Wu, Q Lin, WN Chen Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20892 …, 2024 | 1 | 2024 |