News

2023-9-15 Our four papers have been accepted by IEEE SSCI 2023.

Experiences

2005 - 2009, Xi’an Jiaotong University, Bachelor Degree
2009 - 2016, Xi’an Jiaotong University, PhD Degree
2012 - 2014, The University of Tokushima, Japan, Visiting Scholar
2016.10 - 2016.12, The Hong Kong Polytechnic University, Research Assistant
2017 - 2019, Southern University of Science and Technology, Postdoc Researcher
2019 - 2022, Southern University of Science and Technology, Research Assistant Professor
2023 - now, Southern University of Science and Technology, Research Associate Professor

Journal papers

• 17. Shang K., Shu T., Ishibuchi H., Nan Y., and Pang L. M. “Benchmarking Large-Scale Subset Selection in Evolutionary Multi-Objective Optimization.” Information Sciences (2022).
  16. Shu T., Shang K., Ishibuchi H., and Nan Y. “Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization.” IEEE Transactions on Evolutionary Computation (2022).
  15. Nan Y, Shang K., Ishibuchi H., and He L. “An Improved Local Search Method for Large-Scale Hypervolume Subset Selection.” IEEE Transactions on Evolutionary Computation (2022).
  14. Shang K., Shu T., and Ishibuchi H. “Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation.” IEEE Transactions on Evolutionary Computation (2022).
  13. He L., Shang K., Nan Y., Ishibuchi H., and Srinivasan D. “Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multi-Objective Evolutionary Algorithms.” IEEE Transactions on Evolutionary Computation (2022).
  12. Pang L. M., Ishibuchi H., and Shang K. "Use of Two Penalty Values in Multi-objective Evolutionary Algorithm based on Decomposition." IEEE Transactions on Cybernetics (2022).
  11. Shang K.#, Chen W.#, Liao W., and Ishibuchi H. “HV-Net: Hypervolume Approximation based on DeepSets.” IEEE Transactions on Evolutionary Computation (2022). (#Equal Contribution)
  10. Pang L. M., Ishibuchi H., and Shang K. "Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization." IEEE Transactions on Evolutionary Computation (2022).
  9. Ishibuchi H., Pang L. M., and Shang K. "Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms." IEEE Computational Intelligence Magazine (2021).
  8. Shang K., Ishibuchi H., Chen W., Nan Y., and Liao W. “Hypervolume-Optimal μ-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions”. IEEE Transactions on Evolutionary Computation (2021).
  7. Chen W., Ishibuchi H., and Shang K. “Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization”. IEEE Transactions on Evolutionary Computation (2021).
  6. Shang K., Ishibuchi H., He L., and Pang L. M. “A Survey on the Hypervolume Indicator in Evolutionary Multi-objective Optimization.” IEEE Transactions on Evolutionary Computation (2020). ESI Highly Cited Paper
  5. Shang K., and Ishibuchi H. "A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization." IEEE Transactions on Evolutionary Computation (2020).
  4. Shang K., Ishibuchi H., and Ni X. "R2-based Hypervolume Contribution Approximation." IEEE Transactions on Evolutionary Computation (2020).
  3. He L., Shang K., and Ishibuchi H. "Simultaneous Use of Two Normalization Methods in Decomposition-based Multi-objective Evolutionary Algorithms." Applied Soft Computing (2020).
  2. Nan Y., Shang K., Ishibuchi H. “Reverse Strategy for Non-dominated Archiving.” IEEE Access (2020).
  1. Shang K., Feng Z., Ke L., and Chan F. T. "Comprehensive Pareto Efficiency in robust counterpart optimization." Computers & Chemical Engineering (2016).
  

  Conference papers

  19. An G., Wu Z., Shen Z., Shang K., Ishibuchi H. “Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex Environments.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023).
  18. Ishibuchi H., Pang L. M., Shang K. “Effects of Dominance Modification on Hypervolume-based and IGD-based Performance Evaluation Results of NSGA-II.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023).
  17. Shu T., Nan Y., Shang K., Ishibuchi H. “Two-Phase Procedure for Efficiently Removing Dominated Solutions From Large Solution Sets.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023).
  16. Zhu H., Shang K., Ishibuchi H. “STHV-Net: Hypervolume Approximation based on Set Transformer.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023).
  15. Shu T., Shang K., Nan Y., and Ishibuchi H. “Direction Vector Selection for R2-based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
  14. Shang K., Liao W., and Ishibuchi H. “HVC-Net: Deep Learning based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
  13. Shang K., Ishibuchi H., Pang L. M., and Nan Y. “Reference Point Specification for Greedy Hypervolume Subset Selection.” IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
  12. Pang L. M.#, Shang K.#, Chen L., Ishibuchi H., and Chen W. “Proposal of a New Test Problem for Large-Scale Many-Objective Optimization”. IEEE International Conference on Systems, Man, and Cybernetics (SMC2021). (#Equal Contribution)
  11. Nan Y., Shang K., Ishibuchi H., and He L. “Improving Hypervolume-based Greedy Sequential Insertion Subset Selection in Evolutionary Multi-objective Optimization”. IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
  10. Nan Y., Shang K., Ishibuchi H., and He L. “A Two Stage Hypervolume Contribution Approximation Method Based on R2 Indicator”. IEEE Congress on Evolutionary Computation (CEC2021).
  9. Shang K., Ishibuchi H., and Chen W. “Greedy Approximated Hypervolume Subset Selection for Many-objective Optimization”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021). Best Paper Award
  8. Shang K., Ishibuchi H., and Nan Y. “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
  7. Shang K., Ishibuchi H., Chen L., Chen W., and Pang L. M. “Improving the Efficiency of R2HCA-EMOA”. 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO2021).
  6. Shang K., Ishibuchi H., Nan Y., and Chen W. “Transformation-based Hypervolume Indicator: A Framework for Designing Hypervolume Variants”. IEEE Symposium Series on Computational Intelligence (SSCI2020).
  5. Shang K., Ishibuchi H., Chen W., and Adam L. "Hypervolume optimal mu-distributions on line-based Pareto fronts in three dimensions." Parallel Problem Solving from Nature. (PPSN2020).
  4. Chen W., Ishibuchi H., and Shang K. “Proposal of a realistic many-objective test suite.” Parallel Problem Sovling from Nature. (PPSN2020). Best Paper Nomination
  3. Nan Y.#, Shang K.#, and Ishibuchi H. "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）
  2. Ishibuchi H., Peng Y., and Shang K. "A Scalable Multimodal Multiobjective Test Problem." IEEE Congress on Evolutionary Computation (CEC2019). First Runner-up Conference Paper Award
  1. Shang K., Ishibuchi H., Zhang M. L., and Liu Y. "A new R2 indicator for better hypervolume approximation." Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2018). Best Paper Award
  

Patents

尚可, 石渕久生. 飞行决策生成方法和装置、计算机设备、存储介质. 发明. 实审. 中国. 202210084970.9. 2022/1/25. 南方科技大学.

Invited Talks

1. 演化多目标优化的发展现状及最新研究进展, 中国计算机学会青年计算机科技论坛兰州分论坛, 2020年7月4日. [link]
2. Hypervolume Approximation for Many-objective Optimization, IEEE CIS Seminar, 2022-8-24. [link]
3. 面向高维多目标优化的超体积指标近似, 大数据专委会学术活动-NICE Seminar, 2023-2-19. [link]

Tutorials and Workshops

1. Difficulties in Fair Performance Comparison of Multiobjective Evolutionary Algorithms, GECCO 2022 Tutorial. [link]
2. How to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems. WCCI 2022 Tutorial. [link]
3. Subset Selection in Evolutionary Multi-objective Optimization. WCCI 2022 Workshop. [link]
4. How to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems. IEEE CEC 2023 Tutorial.
5. Quality indicators for multi-objective optimization: performance assessment and algorithm design. IEEE CEC 2023 Tutorial.
6. Hypervolume Approximation for Many-objective Optimization and Learning. ECAI 2023 Tutorial.
7. Hypervolume Approximation for Many-objective Optimization and Learning. ICONIP 2023 Tutorial.

Honors and Awards

1. 2022 IEEE Senior Member
2. 2021 GECCO Best Paper Award (Shang K., Ishibuchi H., Chen W.)
3. 2021 Overseas High-Caliber Personnel in Shenzhen
4. 2020 IEEE CIS Travel Grant
5. 2020 PPSN Best Paper Nomination (Chen W., Ishibuchi H., Shang K.)
6. 2019 CEC First Runner-up Conference Paper Award (Ishibuchi H., Peng Y., Shang K.)
7. 2018 GECCO Best Paper Award (Shang K., Ishibuchi H., Zhang M. L., Liu Y.)

Grants

1. 2021.01-2023.12 Youth Program of NSFC, PI
2. 2020.03-2023.02 Shenzhen postdoctoral research funding, PI