Assistant Professor School of System Design and Intelligent Manufacturing

Zhenkun received his Ph.D. degree 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 interests include metaheuristics algorithm, multiobjective optimization and their application on the traffic management of UAVs. From January 2019 to March 2020, he worked as a Postdoctoral Research Fellow at the Department of Computer Science, City University of Hong Kong. He developed a multiobjective optimization aided decision-making system for large-scale manufacturing planning. From April 2020 to May 2020, he worked as a Research Fellow at the City University of Hong Kong Shenzhen Research Institute, mainly engaged in the research of multiobjective evolutionary optimization algorithms. In June 2020, Zhenkun joined the School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, as an Assistant Professor. As a key member, Dr. Wang participated in several projects funded by the National Natural Science Foundation of China (NSFC), the Civil Aviation Authority of Singapore (CAAS), the French National Research Agency (Agence Nationale de la Recherche, ANR), the Research Grants Council of Hong Kong (HKRGC), Huawei Technologies Co., Ltd, etc. He has published a number of papers in international journals and conferences. He serves as an Associate Editor of Swarm and Evolutionary Computation (JCR Q1). Member of CAAI-YC; EMO 2021 Competition Chair; PC Member of CPSCom 2017, AAAI 2019, ACAIT 2020.

Personal Profile

2020.06 - present, Assitant Professor, School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China

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

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

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

2011.08 – 2016.12, PhD, School of Electronic Engineering, Xidian University, Xi'an, China


1, Multiobjective optimization and decision making;

2, Supply chain management and intelligent optimization;

3, Artificial intelligence assisted optimal design.


SDM223 System Design and Management

Publications Read More

Journal papers:

[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 9.309)

[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 5.91)

[3] Chen Xu, Yiyuan Chai, Sitian Qin, Zhenkun Wang, Jiqiang Feng. “A Neurodynamic Approach to Nonsmooth Pseudoconvex Optimization Problems”, Neural Networks, 124: 180-192, 2020. (NN, IF 5.535)

[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 11.169)

[5] Jianping Luo, Abhishek Gupta, Yew-Soon Ong and Zhenkun Wang. “Evolutionary Optimization of Expensive Multiobjective Problems with Co-sub-Pareto Front Gaussian Process Surrogates”, IEEE Transactions on Cybernetics, 49(5): 1708-1721, 2019. (TCYB, IF 11.079)

[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 11.169)

[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 11.169)

[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. (SWEVO, IF 6.912)

[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 5.91)

[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 11.079) [ESI highly cited paper]

Conference papers

[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 Knapsack 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.

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Join us

The Computational Intelligence and Advanced Manufacturing (CIAM) group is established and led by Dr. Wang Zhenkun. Our group is devoted to the research of Computational Intelligence methods and their applications in Advanced Manufacturing.

Research topics include but not limited to:

  1. Multiobjective optimization and decision-making 

    In this topic, we mainly focus on the research of multiobjective optimization algorithms, multiobjective decision-making systems, and their applications in engineering problems.

  2. Supply chain management and intelligent optimization

    This topic is geared towards the optimization problems in supply chain management, such as the warehouse location problem, logistics planning, and job-shop flow scheduling, and so on. We are aiming to develop novel intelligent optimization algorithms, such as heuristic algorithms and machine learning algorithms, to improve production efficiency, resource utilization, and environmental protection. The ultimate goal is to build a smart supply chain management system that is friendly to the economy, society and environment.

  3. Artificial intelligence assisted optimal design

    Some complex optimization problems (such as aircraft design, antenna design and circuit design) exist in industrial design and manufacturing. We are trying to utilize artificial intelligence assisted technologies (e.g., surrogate model-assisted optimization algorithm, etc.) to improve the design and manufacturing process.

Positions Available:  

  1. International Master and Ph.D. students  (Starting from 2021 Fall)
  2. Postdoctoral Researchers
  3. Research Assistants, Visiting Scholars and Visiting Students

Required/Desired Skills and Qualifications:

  1. Degree in computer science, operational research, math, electronic engineering or other related fields,
  2. Good English listening, speaking, reading and writing skills,
  3. Experience in computational intelligence algorithm development, supply chain management and intelligent optimization will be an advantage,
  4. The postdoctoral position has an age limit of 35 years.

How to apply:

If you are interested in joining us, please send your application to (with the subject line in the following format: Name_ Position _ CIAM).

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Contact Us

Contact Address

Room 608, Building 1, Innovation Park, Southern University of Science and Technology, 1088 Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong

Office Phone



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