王振坤

助理教授

王振坤

wangzk3@sustech.edu.cn

王振坤博士于2016年12月获得西安电子科技大学电路与系统专业博士学位;2017年2月至2019年1月,在新加坡南洋理工大学计算机科学与工程学院担任博士后研究员,主要负责研究启发式算法辅助的多目标优化技术及其在无人机交通调度中的应用;2019年1月-2020年3月,在香港城市大学电脑科学系任博士后研究员,致力于开发一种用于多工厂智能排产的决策辅助系统;2020年4月-5月,在香港城市大学深圳研究院任研究员,主要从事多目标演化优化算法的研究;2020年6月加入南方科技大学系统设计与智能制造学院,任助理教授。
王振坤博士作为核心成员,参与完成多项由国家自然科学基金委员会、新加坡民航局、华为科技有限公司资助的项目。在国际学术期刊及会议上发表论文十余篇。担任Swarm and Evolutionary Computation (JCR 一区) 副主编,以及IEEE TEVC、IEEE TCYB、IEEE TNNLS等多个期刊审稿人。

研究兴趣

多目标优化与决策
供应链管理与智能优化
先进制造中的人工智能

工作经历

2020 年 6 月 – 至今 南方科技大学,系统设计与智能制造学院,助理教授
2020 年 4 月 – 5 月,香港城市大学深圳研究院,研究员
2019 年 1 月 – 2020 年 3 月,香港城市大学,电脑科学系,博士后研究员
2017 年 2 月 – 2019 年 1 月,南洋理工大学,计算机科学与工程学院,博士后研究员

教育经历

2011年-2016年,西安电子科技大学,电子工程学院,博士
2007年-2011年,山东建筑大学,信息与电气工程学院,学士

 

发表文章

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.

博士招生方向

人工智能,计算机科学与技术