Kunjie Yu, Associate Professor
E-mail:yukunjie@zzu.edu.cn
Dr. Kunjie Yu received the Ph.D. degree in control science and engineering from the East China University of Science and Technology, Shanghai, China, in 2017. Currently, he is an associate professor with the School of Electrical Engineering, Zhengzhou University. He has published more than 15 peer-reviewed papers on Applied Energy, Energy, Energy Conversion and management, Information Sciences, Knowledge-Based Systems, Computers & Chemical Engineering, Chemometrics and Intelligent Laboratory Systems, and other related journals. His current research interests include evolutional computation, constrained optimization, multi-objective optimization, and their applications in chemical process, photovoltaic system, and energy system.
Published papers：
[1] Kunjie Yu, Dezheng Zhang, Jing Liang, Ke Chen*, Caitong Yue, Kangjia Qiao, Ling Wang. A Correlation-Guided Layered Prediction Approach for Evolutionary Dynamic Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 2022, doi: 10.1109/TEVC.2022.3193287.
[2] Jing Liang, Kangjia Qiao, Kunjie Yu*, Boyang Qu, Caitong Yue, Weifeng Guo, Ling Wang. Utilizing the relationship between unconstrained and constrained Pareto fronts for constrained multiobjective optimization. IEEE Transactions on Cybernetics, 2022, doi: 10.1109/TCYB.2022.3163759.
[3] Jing Liang, Xuanxuan Ban, Kunjie Yu*, Boyang Qu, Kangjia Qiao, Caitong Yue, Ke Chen, Kay Chen Tan. A Survey on Evolutionary Constrained Multi-objective Optimization. IEEE Transactions on Evolutionary Computation, 2022, doi: 10.1109/TEVC.2022.3155533.
[4] Kunjie Yu, Jing Liang*, Boyang Qu, Yong Luo, Caitong Yue. Dynamic Selection Preference-Assisted Constrained Multiobjective Differential Evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(5): 2954 - 2965.
[5] Kunjie Yu, Jing Liang*, Boyang Qu, Caitong Yue. Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization. Swarm and Evolutionary Computation, 2021,60: 100799.
[6] Kangjia Qiao, Jing Liang, Kunjie Yu*, Minghua Yuan, Boyang Qu, Caitong Yue. Self-adaptive resources allocation-based differential evolution for constrained evolutionary optimization. Knowledge-Based Systems, 2022, 235: 107653.
[7] Jing Liang, Xuanxuan Ban, Kunjie Yu*, Kangjia Qiao, Boyang Qu. Constrained multiobjective differential evolution algorithm with infeasible-proportion control mechanism. Knowledge-Based Systems, 2022: 109105.
[8] Jing Liang, Xuanxuan Ban, Kunjie Yu*, Boyang Qu, Kangjia Qiao. Differential evolution with rankings-based fitness function for constrained optimization problems. Applied Soft Computing, 2021, 113 : 108016.
[9] Jing Liang, Guanlin Chen, Boyang Qu, Kunjie Yu*, Caitong Yue, Kangjia Qiao, Hua Qian*. Cooperative co-evolutionary comprehensive learning particle swarm optimizer for formulation design of explosive simulant. Memetic Computing, 2020, 12: 331–341.
[10] Jing Liang, Kangjia Qiao, Kunjie Yu*, Shile Ge, Boyang Qu, Ruohao Xu, Ke Li. Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution. Solar Energy, 2020, 207: 336-346.
[11] Jing Liang, Kangjia Qiao, Minghua Yuan, Kunjie Yu*, Boyang Qu, Shile Ge, Yaxin Li, Guanlin Chen. Evolutionary multi-task optimization for parameters extraction of photovoltaic models. Energy Conversion and Management, 2020, 207, 112509.
[12] Jing Liang, Shile Ge, Boyang Qu, Kunjie Yu*, Fengjiao Liu, Haotian Yang, Panpan Wei, Zhimeng Li. Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models. Energy Conversion and Management, 2020, 203, 112138.
[13] Kunjie Yu, Boyang Qu, Caitong Yue, Shilei Ge, Xu Chen, Jing Liang*, A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module, Applied Energy, 2019, 237: 241-257.
[14] Kunjie Yu, Lyndon While, Mark Reynolds, Xin Wang, J.J. Liang, Liang Zhao*, Zhenlei Wang*. Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization. Energy, 2018, 148: 469-481.
[15] Kunjie Yu, J.J. Liang*, B.Y. Qu, Zhiping Cheng, Heshan Wang. Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Applied Energy, 2018, 226: 408-422.
[16] Kunjie Yu, J.J. Liang*, B.Y. Qu, Xu Chen, Heshan Wang. Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion and Management, 2017, 150: 742-753.
[17] Kunjie Yu, Xu Chen, Xin Wang, Zhenlei Wang*. Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization. Energy Conversion and Management, 2017, 145: 233-246.
[18] Kunjie Yu, Xin Wang, Zhenlei Wang*. Constrained optimization based on improved teaching-learning-based optimization algorithm. Information Sciences, 2016, 352-353: 61-78.
[19] Kunjie Yu, Lyndon While, Mark Reynolds, Xin Wang, Zhenlei Wang. Cyclic Scheduling for an Ethylene Cracking Furnace System using Diversity Learning Teaching-learning-based Optimization. Computers & Chemical Engineering, 2017, 99: 314-324.
[20] Kunjie Yu, Xin Wang, Zhenlei Wang*. An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing, 2016, 27(4): 831-843.
[21] Kunjie Yu, Xin Wang, Zhenlei Wang. Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization. Knowledge-Based Systems, 2016, 96: 156-170.
[22] Kunjie Yu, Xin Wang, Zhenlei Wang. Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemometrics and Intelligent Laboratory Systems, 2015, 146: 198-210.
[23] 于坤杰, 王昕, 王振雷.基于反馈的精英教学优化算法.自动化学报, 2014, 40(9): 1976-1983.
[24] 于坤杰, 王昕, 王振雷.改进的教学优化算法及其应用.化工进展, 2014, 33(4): 850-854.
[25] 梁静, 葛士磊, 瞿博阳, 于坤杰*. 求解电力系统经济调度问题的改进粒子群优化算法.控制与决策, 2020, 35 (8): 1813-1822.