Dr. Wang Zhenkun received the PhD degree in circuits and systems from Xidian University in December 2016. From February 2017 to January 2019, he worked as a research fellow at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. His research was mainly about heuristic algorithm-assisted multiobjective optimization and its application in traffic scheduling and management of UAVs. As a key member, Dr. Wang participated in the projects funded by the National Natural Science Foundation of China, the Civil Aviation Authority of Singapore, and Huawei Technologies Co., Ltd. He has published more than ten papers in international journals and conferences. He served as the Associate Editor of Swarm and Evolutionary Computation (JCR Q1), as well as reviewers of IEEE TEVC, IEEE TCYB, IEEE TNNLS and other journals.

Research Area

Multiobjective optimization and decision-making

Supply chain management and intelligent optimization

Artificial intelligence assisted optimal design

Working Experience

2020.06 - present, Assitant Professor, School of System Design and Intelligent Manufacturing

2020.04 - 2020.05，Research Fellow，Shenzhen Research Institute of City University of Hong Kong

2019.01 - 2020.03，Postdoctoral Research Fellow, Department of Computer Science, City University of Hong Kong

2017.02 - 2019.01, Research Fellow, School of Computer Science and Engineering, Nanyang Technological University

Education Background

2011 - 2016  PhD, School of Electronic Engineering, Xidian University

2007 - 2011  Bachelor, School of Information and Electrical Engineering，Shandong Jianzhu University

Publications

Journal publications

[1] Weifeng Gao, Genghui Li, Qingfu Zhang, Yuting Luo and Zhenkun Wang. “Solving Nonlinear Equation Systems by a Two-Phase Evolutionary Algorithm”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press.(TSMC, IF: 7.351)

[2] Jianping Luo, Xiongwen Huang, Yun Yang, Xia Li, Zhenkun Wang, Jiqiang Feng. “A Many-objective Particle Swarm Optimizer based on Indicator and Direction Vectors for Many-objective Optimization”. Information Sciences, 514: 166-202 2020.(INS, IF: 5.524)

[3] Chen Xu, Yiyuan Chai, Sitian Qin, Zhenkun Wang, Jiqiang Feng. “A Neurodynamic Approach to Nonsmooth Pseudoconvex Optimization Problems” , Neural Networks, 124: 180-192.(NN, IF: 7.197)

[4] Hao Li, Yew-Soon Ong, Maoguo Gong and Zhenkun Wang. “Evolutionary Multitasking Sparse Reconstruction: Framework and Case Study”, IEEE Transactions on Evolutionary Computation, 23(5): 733-747, 2019.(TEVC, IF: 8.508)

[5] Jianping Luo, Abhishek Gupta, Yew-Soon Ong and Zhenkun Wang. “Evolutionary Optimization of Expensive Multi-objective Problems with Gaussian Co-subPF Surrogates”, IEEE Transactions on Cybernetics, 49(5): 1708-1721, 2019.(TCYB, IF: 10.387)

[6] Zhenkun Wang, Yew-Soon Ong, Jianyong Sun, Abhishek Gupta and Qingfu Zhang. “A Generator for Multiobjective Test Problems with Difficult-to-Approximate Pareto Front Boundaries” IEEE Transactions on Evolutionary Computation, 23(4): 556-571, 2019.(TEVC, IF: 8.508)

[7] Zhenkun Wang, Yew-Soon Ong and Hisao Ishibuchi. “On Scalable Multiobjective Test Problems with Hardly-dominated Boundaries”, IEEE Transactions on Evolutionary Computation, 23(2): 217-231, 2019.(TEVC, IF: 8.508)  

[8] Zhenkun Wang, Qingfu Zhang, Hui Li, Hisao Ishibuchi and Licheng Jiao. “On The Use of Two Reference Points in Decomposition Based Multiobjective Evolutionary Algorithms,”, Swarm and Evolutionary Computation, 34: 89-102, 2017.(Swarm & EC, IF: 6.33)

[9] Maoguo Gong, Yue Wu, Qing Cai, Wenping Ma, Kai Qin, Zhenkun Wang and Licheng Jiao. “Discrete Particle Swarm Optimization for High-order Graph Matching”, Information Sciences, 328: 158-171 2016.(INS, IF: 5.524)

[10] Zhenkun Wang, Qingfu Zhang, Aimin Zhou, Maoguo Gong and Licheng Jiao. Adaptive Replacement Strategies for MOEA/D, IEEE Transactions on Cybernetics, 46(2): 474-486, 2016.(TCYB, IF: 10.387) [ESI highly cited paper]

Conference publications

[1] Qingyu Tan, Zhenkun Wang, Yew-Soon Ong, Kin Huat Low. “Evolutionary Optimization-based Mission Planning for UAS Traffic Management (UTM)”, 2019 International Conference on Unmanned Aircraft Systems, p. 952-958, (ICUAS) 2019.
[2] Mohamed Faisal B Mohamed Salleh, Wanchao Chi, Zhenkun Wang, Shuangyao Huang, Da-Yang Tan, Tingting Huang, Kin Huat Low. “Preliminary Concept of Adaptive Urban Airspace Management for Unmanned Aircraft Operations” AIAA Information Systems-AIAA Infotech@ Aerospace p. 2260, (AIAA) 2018.
[3] Xingxing Hao, Jing Liu, Zhenkun Wang. “An Improved Global Replacement Strategy for MOEA/D on Many-objective Kanpsack Problems.” 2017 IEEE Congress on Automation Science and Engineering, p. 624-629, (CASE) 2017.
[4] Improved Adaptive Global Replacement Scheme for MOEA/D-AGR.
Hiu-Hin Tam, Man-Fai Leung, Zhenkun Wang, Sin-Chun Ng, Chi-Chung Cheung, Andrew K Lui.
In 2016 IEEE Congress on Evolutionary Computation, p. 2153-2160, (CEC) 2016.
[5] Zhenkun Wang, Qingfu Zhang, Hui Li. “Balancing Convergence and Diversity by Using Two Different Reproduction Operators in MOEA/D: Some Preliminary Work.” 2015 IEEE Conference on Systems, Mans and Cybernetics, p. 2849–2854. (SMC) 2015.
[6] Zhenkun Wang, Qingfu Zhang, Maoguo Gong, Aimin Zhou. “A Replacement Strategy for Balancing Convergence and Diversity in MOEA/D.” 2014 IEEE Congress on Evolutionary Computation, p. 2132-2139, (CEC) 2014.