papers:
Journal papers: (after joining SUSTech)
[1] Qiang He, Lihong Sun, Xingwei Wang*, Zhenkun Wang, Min Huang, Bo Yi, Yuantian Wang, Lianbo Ma, "Positive opinion maximization in signed social networks", Information Sciences 558, 34-49, 2021. (INS, IF 8.233)
[2] Hui-Ling Zhen, Zhenkun Wang*, Xijun Li, Qingfu Zhang, Mingxuan Yuan, Jia Zeng, "Accelerate the optimization of large-scale manufacturing planning using game theory", Complex & Intelligent Systems, 8(4), 2719-2730, 2022 (CAIS, IF 6.7)
[3] Qite Yang, Zhenkun Wang*, Jianping Luo, Qiang He, "Balancing performance between the decision space and the objective space in multimodal multiobjective optimization", Memetic Computing 13(1), 31-47, 2021. (MC, IF 3.577)
[4] Zhenkun Wang*, Hui-Ling Zhen, Jingda Deng, Qingfu Zhang, Xijun Li, Mingxuan Yuan, Jia Zeng, "Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing Planning", IEEE Transactions on Cybernetics, 52(8), 8326-8339, 2022. (TCYB, IF 19.118)
[5] Weijing Dai, Zhenkun Wang*, Ke Xue, "System-in-Package Design using Multi-task Memetic Learning and Optimization", Memetic Computing, 14(1), 45-59, 2022. (MC, IF 3.577)
[6] Zhenkun Wang*, Qingyan Li, Qite Yang, Hisao Ishibuchi, "The dilemma between eliminating dominance resistant solutions and preserving boundary solutions of extremely convex Pareto fronts", Complex & Intelligent Systems, in press 2021. (CAIS, IF 6.7)
[7] Genghui Li, Qingfu Zhang*, Zhenkun Wang*, "Evolutionary Competitive Multitasking Optimization", IEEE Transactions on Evolutionary Computation, 26(2), 278-289, 2022. (TEVC, IF 16.497)
[8] Zhenkun Wang*, Qingfu Zhang, Yew-Soon Ong, Shunyu Yao, Haitao Liu, Jianping Luo, "Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization", IEEE Transactions on Cybernetics, 53(1), 483-496, 2023. (TCYB, IF 19.118)
[9] 高卫峰，刘玲玲，王振坤，公茂果，“基于分解的演化多目标优化算法综述”，软件学报，2022。
[10] Zhongju Yuan†, Genghui Li†, Zhenkun Wang*, Jianyong Sun, Ran Cheng, "RL-CSL: A Combinatorial Optimization Method Using Reinforcement Learning and Contrastive Self-supervised Learning", IEEE Transactions on Emerging Topics in Computational Intelligence, in press 2022. (TETCI, IF 4.851)
[11] Lei Cao, Chunming Ye, Ran Cheng*, Zhenkun Wang, "Memory-based Variable Neighborhood Search for Green Vehicle Routing Problem with Passing-by Drivers: A Comprehensive Perspective", in press 2022. (CAIS, IF 6.7)
[12] Genghui Li, Zhenkun Wang*, Qingfu Zhang, Jianyong Sun, Offline and Online Objective Reduction via Gaussian Mixture Model Clustering, IEEE Transactions on Evolutionary Computation, in press 2022. (TEVC, IF 16.497）
[13] Wencheng Han, Hao Li*, Maoguo Gong*, Jianzhao Li, Yiting Liu, Zhenkun Wang, Multi-swarm particle swarm optimization based on CUDA for sparse reconstruction, Swarm and Evolutionary Computation, in press 2022. (SWEVO, IF 10.267）
[14] Zhenkun Wang†, Shuangchun Gui†, Xingpeng Ding, Xiaowei Hu*, Xiaowei Xu*, Xiaomeng Li, "Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis", IEEE Transactions on Emerging Topics in Computational Intelligence, in press 2022. (TETCI, IF 4.851)
[15] Genghui Li, Zhenkun Wang*, Maoguo Gong, "Expensive Optimization via Surrogate-Assisted and Model-Free Evolutionary Optimization", IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press 2022. (TSMCA, IF 11.471)
[16] Xin Liu, Jianyong Sun*, Qingfu Zhang, Zhenkun Wang, Zongben Xu , "Learning to Learn Evolutionary Algorithm: A Learnable Differential Evolution", IEEE Transactions on Emerging Topics in Computational Intelligence, in press 2023. (TETCI, IF 4.851)
[17] Jixiang Chen, Fu Luo, Genghui Li, Zhenkun Wang*, "Batch Bayesian Optimization with Adaptive Batch Acquisition Functions via Multi-objective Optimization", Swarm and Evolutionary Computation, in press, 2023. (SWEVO, IF 10.267)
[18] Genghui Li†, Lindong Xie†, Zhenkun Wang*, Huajun Wang, Maoguo Gong, "Evolutionary Algorithm with Individual-Distribution Search Strategy and Regression-Classification Surrogates for Expensive Optimization", Information Sciences, in press, 2023. (INS, IF 8.233)
Conference papers: (after joining SUSTech)
[1] Qite Yang, Zhenkun Wang*, Hisao Ishibuchi, "It Is Hard to Distinguish Between Dominance Resistant Solutions and Extremely Convex Pareto Optimal Solutions", The 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021, Shenzhen, China, March 28–31, 2021.
[2] Zhenkun Wang, Jingda Deng*, Qingfu Zhang, Qite Yang, "On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D", The 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021, Shenzhen, China, March 28–31, 2021.
[3] Weijing Dai, Zhenkun Wang, Ke Xue, "Multi-objectives Design Optimization based on Multi-objectives Gaussian Processes for System-in-Package", IEEE 71st Electronic Components and Technology Conference, ECTC 2021, San Diego, California, United States, June 1–4, 2021.
[4] Jixiang Chen†, Fu Luo†, Zhenkun Wang*, "Dynamic Multi-objective Ensemble of Acquisition Functions in Batch Bayesian Optimization”, The Genetic and Evolutionary Computation Conference Companion, GECCO 2022, Boston Massachusetts, United States, July 9-13, 2022.
[5] Genghui Li, Qingyan Li, Zhenkun Wang*, "Adaptive Differential Evolution Algorithm with Multiple Gaussian Learning Models", The CAAI International Conference on Artificial Intelligence, CACAI 2022, Beijing, China, August 27-28, 2022.
[6] Ziliang Miao†, Buwei He†, Hubocheng Tang†, Jixiang Chen, Zhenkun Wang*, "Stacked Ensemble of Metamodels for Expensive Global Optimization", The 8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022, Chengdu, China, November 26-28, 2022.
[7] Ruihao Zheng, Zhenkun Wang*, "A Generalized Scalarization Method for Evolutionary Multi-objective Optimization", The 37th AAAI Conference on Artificial Intelligence, AAAI-2023, Washington DC, United States, February, 7-14, 2023. [Oral CCF-A]
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Journal papers: (before joining SUSTech)
[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, 51(9): 5652-5663, 2021. (TSMC, IF 11.471)
[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 8.233)
[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 9.657)
[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 16.497)
[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 19.118)
[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 16.497)
[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 16.497)
[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 10.267)
[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 8.233)
[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 19.118) [ESI highly cited paper]
Conference papers: (before joining SUSTech)
[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, 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.