Distinguished Research Fellow
Shang Ke, PhD, Distinguished Research Fellow, Assistant Professor, and Doctoral Supervisor at Shenzhen University.
Email: shangk@szu.edu.cn or kshang@foxmail.com
PhD received from Xi'an Jiaotong University.
He has led one National Natural Science Foundation of China (NSFC) Youth Project and one NSFC General Project, as well as one Guangdong Provincial Natural Science Foundation General Project. He has published over 70 papers as first/corresponding author in important journals and international conferences such as IEEE TEVC, IEEE TCYB, IEEE CIM, IJCAI, PPSN, and GECCO, with over 1500 citations on Google Scholar. He received the Best Paper Award at ACM GECCO 2018/2021/2024, the runner-up Best Paper Award at IEEE CEC 2019, and a Best Paper nomination at PPSN 2020. He is currently a Senior Member of IEEE.
He plans to recruit 3 Master's students in 2025 (including an academic Master's degree in Computer Science and Technology and a professional Master's degree in Computer Technology). Students interested in artificial intelligence, large-scale models, and intelligent optimization algorithms are welcome to contact him. Research Capabilities and Achievements
Research Interests: Multi-objective optimization, computational intelligence, reinforcement learning, large-model-based optimization, etc.
Journal Publications
Hypervolume Approximation based on DeepSets.” IEEE Transactions on Evolutionary Computation (2022). (#Equal Contribution)
4. K. Shang, H. Ishibuchi, W. Chen, Y. Nan, and W. Liao “Hypervolume-Optimal μ-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions”. IEEE Transactions on Evolutionary Computation (2021).
5. K. Shang, H. Ishibuchi, L. He, and L. M. Pang “A Survey on the Hypervolume Indicator in Evolutionary Multi-objective Optimization.” IEEE Transactions on Evolutionary Computation (2020). ESI Highly Cited Paper
6. K. Shang, and H. Ishibuchi ""A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization."" IEEE Transactions on Evolutionary Computation (2020).
7. K. Shang, H. Ishibuchi, and X. Ni ""R2-based Hypervolume Contribution Approximation."" IEEE Transactions on Evolutionary Computation (2020).
8. K. Shang, Z. Feng, L. Ke, and F. T. Chan ""Comprehensive Pareto Efficiency in robust counterpart optimization."" Computers & Chemical Engineering (2016).
9. T. Shu, K. Shang*, H. Ishibuchi*, and Y. Nan “Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization.” IEEE Transactions on Evolutionary Computation (2022). (*Corresponding author)
10. Y. Nan, K. Shang, H. Ishibuchi, and L. He “An Improved Local Search Method for Large-Scale Hypervolume Subset Selection.” IEEE Transactions on Evolutionary Computation (2022).
11. L. He, K. Shang, Y. Nan, H. Ishibuchi, and D. Srinivasan “Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multi-Objective Evolutionary Algorithms.” IEEE Transactions on Evolutionary Computation (2022).
12. L. He, K. Shang, and H. Ishibuchi ""Simultaneous Use of Two Normalization Methods in Decomposition-based Multi-objective Evolutionary Algorithms."" Applied Soft Computing (2020).
13. Y. Nan, K. Shang, H. Ishibuchi “Reverse Strategy for Non-dominated Archiving.” IEEE Access (2020).
14. L. M. Pang, H. Ishibuchi, and K. Shang ""Use of Two Penalty Values in Multi-objective Evolutionary Algorithm based on Decomposition."" IEEE Transactions on Cybernetics (2022).
15. L. M. Pang, H. Ishibuchi, and K. Shang ""Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization."" IEEE Transactions on Evolutionary Computation (2022).
16. H. Ishibuchi, L. M. Pang, and K. Shang ""Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms."" IEEE Computational Intelligence Magazine (2021).
17. W. Chen, H. Ishibuchi, and K. Shang “Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization”. IEEE Transactions on Evolutionary Computation (2021).
Conference Papers
0. T. Shu, K. Shang*, C. Gong, Y. Nan, and H. Ishibuchi, “Learning Pareto Set for Multi-Objective Continuous Robot Control.” IJCAI 2024.
1. K. Shang, W. Liao, and H. Ishibuchi “HVC-Net: Deep Learning based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
2. K. Shang, H. Ishibuchi, L. M. Pang, and Y. Nan “Reference Point Specification for Greedy Hypervolume Subset Selection.” IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
3. K. Shang, H. Ishibuchi, and W. Chen “Greedy Approximated Hypervolume Subset Selection for Many-objective Optimization”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021). Best Paper Award
4. K. Shang, H. Ishibuchi, and Y. Nan “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
5. K. Shang, H. Ishibuchi, L. Chen, W. Chen, and L. M. Pang “Improving the Efficiency of R2HCA-EMOA”. 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO2021).
6. K. Shang, H. Ishibuchi, Y. Nan, and W. Chen “Transformation-based Hypervolume Indicator: A Framework for Designing Hypervolume Variants”. IEEE Symposium Series on Computational Intelligence (SSCI2020).
7. K. Shang, H. Ishibuchi, W. Chen, and L. Adam ""Hypervolume optimal mu-distributions on line-based Pareto fronts in three dimensions."" Parallel Problem Solving from Nature. (PPSN2020).
8. K. Shang, H. Ishibuchi, M. L. Zhang, and Y. Liu ""A new R2 indicator for better hypervolume approximation."" Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2018). Best Paper Award
9. H. Zhu, K. Shang*, H. Ishibuchi* “STHV-Net: Hypervolume Approximation based on Set Transformer.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023). (*Corresponding author)
10. T. Shu, K. Shang*, Y. Nan, and H. Ishibuchi* “Direction Vector Selection for R2-based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022). (*Corresponding author)
11. L. M. Pang#, K. Shang #, L. Chen, H. Ishibuchi, and W. Chen “Proposal of a New Test Problem for Large-Scale Many-Objective Optimization”. IEEE International Conference on Systems, Man, and Cybernetics (SMC2021). (#Equal Contribution)
12. Y. Nan, K. Shang, H. Ishibuchi, and L. He “Improving Hypervolume-based Greedy Sequential Insertion Subset Selection in Evolutionary Multi-objective Optimization”. IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
13. Y. Nan, K. Shang, H. Ishibuchi, and L. He “A Two Stage Hypervolume Contribution Approximation Method Based on R2 Indicator”. IEEE Congress on Evolutionary Computation (CEC2021).
14. Y. Nan#, K. Shang #, and H. Ishibuchi ""What is a Good Direction Vector Set for the R2-based Hypervolume Contribution Approximation."" Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2020). (#Equal Contribution)
15. W. Chen, H. Ishibuchi, and K. Shang “Proposal of a realistic many-objective test suite.” Parallel Problem Sovling from Nature. (PPSN2020). Best Paper Nomination
16. H. Ishibuchi, Y. Peng, and K. Shang ""A Scalable Multimodal Multiobjective Test Problem."" IEEE Congress on Evolutionary Computation (CEC2019). First Runner-up Conference Paper Award
17. T. Shu, Y. Nan, K. Shang*, H. Ishibuchi* “Two-Phase Procedure for Efficiently Removing Dominated Solutions from Large Solution Sets.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023). (*Corresponding author)
18. G. An, Z. Wu, Z. Shen, K. Shang*, H. Ishibuchi* “Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex Environments.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023). (*Corresponding author)
Invited Presentations
1. Current Status and Latest Research Progress in Evolutionary Multi-Objective Optimization, Lanzhou Branch Forum of the China Computer Federation Young Computer Scientists Forum, July 4, 2020.
2. Hypervolume Approximation for Many-objective Optimization, IEEE CIS Seminar, August 24, 2022.
3. Hypervolume Index Approximation for High-Dimensional Multi-Objective Optimization, Big Data Special Committee Academic Activity - NICE Seminar, February 19, 2023.
Tutorials
1. Difficulties in Fair Performance Comparison of Multiobjective Evolutionary Algorithms, GECCO 2022 Tutorial.
2. How to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems. WCCI 2022 Tutorial.
3. How to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems. IEEE CEC 2023 Tutorial.
4. Quality indicators for multi-objective optimization: performance assessment and algorithm design. IEEE CEC 2023 Tutorial.
5. Hypervolume Approximation for Many-objective Optimization and Learning. ECAI 2023 Tutorial.
6. Hypervolume Approximation for Many-objective Optimization and Learning. ICONIP 2023 Tutorial.
Patents
Shang Ke, Hisao Ishibuchi. Flight decision generation method and apparatus, computer equipment, storage medium. Invention. Substantive examination. China. 202210084970.9. 2022/1/25. Southern University of Science and Technology.
Project Status
1. 2021.01-2023.12 National Natural Science Foundation of China (NSFC) Youth Project, Research on key issues of hypervolume index in evolutionary multi-objective optimization algorithm, Principal Investigator
2. 2025.01-2028.12 NSFC General Program, Evolution-assisted Pareto solution set learning algorithm and its application, Principal Investigator
3. 2025.01-2027.12 Guangdong Provincial Natural Science Foundation General Program, Research on hypervolume subset selection algorithm based on large model, Principal Investigator
4. 2019.01-2022.12 NSFC General Program, 5. Dynamic High-Dimensional Multi-Objective Evolutionary Optimization Methods and Applications for Complex Pareto Frontiers, Core Member
2024.01-2027.12 National Natural Science Foundation of China (NSFC) General Program: Research on Evolutionary Multimodal Multi-Objective Optimization Algorithms for Subset Selection, Core Member
Awards
2024 Second Prize, National Industrial Internet Innovation Competition
2024 Best Paper Award, GECCO
2022 IEEE Senior Member
2021 Best Paper Award, GECCO
2020 PPSN Best Paper Nomination
2019 Second Place, CEC Best Paper Award
2018 Best Paper Award, GECCO
