Ke Shang (尚可)研究助理教授（副研究员、硕导）I am a Research Assistant Professor in Prof.Hisao Ishibuchi's group at Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech).
Currently I mainly focus on evolutionary multi-objective optimization.

Email: kshang@foxmail.com or shangk@sustech.edu.cn

Office: Room 913, Nanshan i-Park building A7, Xueyuan Road, Nanshan, Shenzhen, China





Welcome! If you are interested in my research, don’t hesitate to contact me.
News2021-5-2 Our paper “Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization”has been conditionally accpeted to TEVC.
2021-4-30 Our paper“Greedy Approximated Hypervolume Subset Selection for Many-objective Optimization”has been nominated for a best paper award by GECCO2021.
2021-4-6 Three of our papers have been accepted as oral presentations to CEC2021.
2021-3-26 Three of our papers have been accepted as full papers to GECCO2021.

Experiences2005 - 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 - now, Southern University of Science and Technology, Research Assistant Professor
PublicationsJournal papers1. 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 (conditionally accepted).
2. Shang K., Ishibuchi H., Chen W., Nan Y., and Liao W. “Hypervolume-Optimal μ-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions”.https://arxiv.org/abs/2104.09736.
3. 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).
4. Shang K., and Ishibuchi H. "A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization." IEEE Transactions on Evolutionary Computation (2020).
5. Shang K., Ishibuchi H., and Ni X. "R2-based Hypervolume Contribution Approximation." IEEE Transactions on Evolutionary Computation (2020).
6. Nan Y., Shang K., Ishibuchi H. “Reverse Strategy for Non-dominated Archiving.” IEEE Access (2020).
7. He L., Shang K., and Ishibuchi H. "Simultaneous Use of Two Normalization Methods in Decomposition-based Multi-objective Evolutionary Algorithms." Applied Soft Computing (2020).
8. Shang K., Chan F. T., Karungaru S., et al. "Two-stage Robust Optimization for Orienteering Problem with Stochastic Weights." Complexity (2020).
9. Shang K., Feng Z., Ke L., and Chan F. T. "Comprehensive Pareto Efficiency in robust counterpart optimization." Computers & Chemical Engineering (2016).
10. Ke L., Xu Z., Feng Z., Shang K., Qian X. “Proportion-based robust optimization and team orienteering problem with interval data.” European Journal of Operational Research (2013).
11. Ke L., Shang K., Feng Z. “On the Model and Optimization Algorithm for Dynamic Team Orienteering Problem.” Journal of Xi'an Jiaotong University (2011). (In Chinese).

Conference papers1. 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).
2. Pang L. M., Ishibuchi H., and Shang K. “Using a Genetic Algorithm-based Hyper-heuristic to Tune MOEA/D for a Set of Various Test Problems”.IEEE Congress on Evolutionary Computation (CEC2021).
3. Chen L., Pang L. M., Ishibuchi H., and Shang K. “Periodical Generation Update using an Unbounded External Archive for Multi-Objective Optimization”. IEEE Congress on Evolutionary Computation (CEC2021).
4.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).
5. Shang K., Ishibuchi H., and Nan Y. “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
6. Zhang J., Ishibuchi H., Shang K., He L., Pang L. M. and Peng Y. “Solutions selection using fuzzy classifier for multiobjective evolutionary algorithms”. 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).
8. Pang L. M., Ishibuchi H., and Shang K. “Using a Genetic Algorithm-based Hyper-heuristic to tune MOEA/D for a set of benchmark test problems”.11th International Conference on Evolutionary Multi-Criterion Optimization (EMO2021).
9. 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).
10. Chen L., Shang K., and Ishibuchi H. “Performance Comparison of Multi-objective Evolutionary Algorithms on Simple and Difficult Many-objective Test Problems”. IEEE Symposium Series on Computational Intelligence (SSCI2020).
11. Ishibuchi H., Pang L. M., and Shang K. "Population Size Specification for Fair Comparison of Multi-Objective Evolutionary Algorithms." IEEE International Conference on Systems, Man, and Cybernetics (SMC2020).
12. Ishibuchi H., Pang L. M., and Shang K. "Numerical Analysis on Optimal Distributions of Solutions for Hypervolume Maximization." IEEE International Conference on Systems, Man, and Cybernetics (SMC2020).
13. Pang L. M., Ishibuchi H., and Shang K. "Algorithm Configurations of MOEA/D with an Unbounded External Archive." IEEE International Conference on Systems, Man, and Cybernetics (SMC2020).
14. Liao W., Ishibuchi H., Pang L. M., and Shang K. "Parallel Implementation of MOEA/D with Parallel Weight Vectors for Feature Selection" IEEE International Conference on Systems, Man, and Cybernetics (SMC2020).
15. 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).
16. Chen W., Ishibuchi H., and Shang K. “Proposal of a realistic many-objective test suite.” Parallel Problem Sovling from Nature. (PPSN2020). (Best Paper Nomination)
17. 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）
18. Chen W., Ishibuchi H., and Shang K."Modified Distance-based Subset Selection for Evolutionary Multi-objective Optimization Algorithms." IEEE Congress on Evolutionary Computation (CEC2020).
19. Chen W., Ishibuchi H., and Shang K. "Lazy greedy hypervolume subset selection from large candidate solution sets." IEEE Congress on Evolutionary Computation (CEC2020).
20. Ishibuchi H., Pang L. M., and Shang K. "A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive." The 24th European Conference on Artificial Intelligence (ECAI2020).
21. Liao W., Shang K., Pang L. M., and Ishibuchi H. "Weak Convergence Detection-based Dynamic Reference Point Specification in SMS-EMOA." IEEE Symposium Series on Computational Intelligence (SSCI2019).
22. 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）
23. Ishibuchi H., He L., and Shang K. "Regular Pareto Front Shape is not Realistic." IEEE Congress on Evolutionary Computation (CEC2019).
24. 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）
25. Ni X., Ishibuchi H., Wan K., Shang K., and Zhuang C. "Weight vector grid with new archive update mechanism for multi-objective optimization." Proceedings of the Genetic and Evolutionary Computation Conference Companion. (GECCO2018).
26. Liu Y., Ishibuchi H., Nojima Y., Masuyama N., and Shang K. "A Double-Niched Evolutionary Algorithm and Its Behavior on Polygon-Based Problems." Parallel Problem Solving from Nature. (PPSN2018).
27. Liu Y., Ishibuchi H., Nojima Y., Masuyama N., and Shang K. "Improving 1by1EA to Handle Various Shapes of Pareto Fronts." Parallel Problem Solving from Nature. (PPSN2018).
28. Shang K., et al. “An Event-Driven Based Multiple Scenario Approach for Dynamic and Uncertain UAV Mission Planning.” International Conference on Swarm Intelligence (ICSI2015).
29. Shang K., Karungaru S., Feng Z., et al. “A GA-ACO hybrid algorithm for the multi-UAV mission planning problem.” International Symposium on Communications and Information Technologies (ISCIT2014).
30. Shang K., Karungaru S., Feng Z., et al. “Periodic re-optimization based dynamic branch and price algorithm for dynamic multi-UAV path planning.” IEEE International Conference on Mechatronics and Automation (ICMA2013).
31. Shang K., Feng Z., Ke L. “An ant colony algorithm for permutation flow shop problem.” World Congress on Intelligent Control and Automation (WCICA2012).

Awards1. 2021 Overseas High-Caliber Personnel in Shenzhen
2. 2020 IEEE CIS Travel Grant
3. 2020PPSN Best Paper Nomination (Chen W., Ishibuchi H., Shang K.)
4. 2019CEC First Runner-up Conference Paper Award (Ishibuchi H., Peng Y., Shang K.)
5. 2018GECCO Best Paper Award (Shang K., Ishibuchi H., Zhang M. L., Liu Y.)

Grants1. 2021.01-2023.12Youth Program of NSFC, PI
2. 2020.03-2023.02Shenzhen postdoctoral research funding, PI

Last updated: 2021-4-30