23. Pang L. M., Ishibuchi H., He L., Shang K., Chen L. “Hypervolume-Based Cooperative Coevolution with Two Reference Points for Multi-Objective Optimization.” IEEE Transactions on Evolutionary Computation (2022).
22. 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).
21. 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).
20. 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).
19. 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).
18. 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).
17. 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).
16. Shang K.#, Chen W.#, Liao W., and Ishibuchi H. “HV-Net: Hypervolume Approximation based on DeepSets.” IEEE Transactions on Evolutionary Computation (2022). (#Equal Contribution)
15. Pang L. M., Ishibuchi H., and Shang K. "Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization." IEEE Transactions on Evolutionary Computation (2022).
14. Ishibuchi H., Pang L. M., and Shang K. "Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms." IEEE Computational Intelligence Magazine (2021).
13. 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).
12. 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).
11. 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).
10. Shang K., and Ishibuchi H. "A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization." IEEE Transactions on Evolutionary Computation (2020).
9. Shang K., Ishibuchi H., and Ni X. "R2-based Hypervolume Contribution Approximation." IEEE Transactions on Evolutionary Computation (2020).
8. 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).
6. Shang K., Chan F. T., Karungaru S., et al. "Two-stage Robust Optimization for the Orienteering Problem with Stochastic Weights." Complexity (2020).
5. Pang L. M., Ishibuchi H., and Shang K. “NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems”. IEEE Access (2020).
4. Pang L. M., Ishibuchi H., and Shang K. “Decomposition-Based Multi-Objective Evolutionary Algorithm Design Under Two Algorithm Frameworks”. IEEE Access (2020).
3. Shang K., Feng Z., Ke L., and Chan F. T. "Comprehensive Pareto Efficiency in robust counterpart optimization." Computers & Chemical Engineering (2016).
2. 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).
1. 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).
46. Wu G., Shu T., Shang K., Ishibuchi H. “Normalization in R2-based Hypervolume and Hypervolume Contribution Approximation.” IEEE Symposium Series on Computational Intelligence (SSCI2023).
45. Wu G., Shu T., Nan Y., Shang K., Ishibuchi H. “Ensemble R2-based Hypervolume Contribution Approximation." IEEE Symposium Series on Computational Intelligence (SSCI2023).
44. Shu T., Nan Y., Shang K., Ishibuchi H. “Analysis of Partition Methods for Dominated Solution Removal from Large Solution Sets.” IEEE Symposium Series on Computational Intelligence (SSCI2023).
43. Shang K., Shu T., Wu G., Nan Y., Pang L. M., Ishibuchi H. “Empirical Hypervolume Optimal μ-Distributions on Complex Pareto Fronts.” IEEE Symposium Series on Computational Intelligence (SSCI2023).
42. 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).
41. 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).
40. 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).
39. Zhu H., Shang K., Ishibuchi H. “STHV-Net: Hypervolume Approximation based on Set Transformer.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023).
38. Nan Y., Ishibuchi H., Shu T., and Shang K. “Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems.” 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO2023).
37. Shu T., Shang K., Nan Y., and Ishibuchi H. “Direction Vector Selection for R2-based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
36. Shang K., Liao W., and Ishibuchi H. “HVC-Net: Deep Learning based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
35. 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).
34. Chen W., Ishibuchi H., and Shang K. “Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization.” IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
33. 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)
32. 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).
31. 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).
30. 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).
29. 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).
28. 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）
27. Shang K., Ishibuchi H., and Nan Y. “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
26. 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).
25. 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).
24. 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).
23. 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).
22. 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).
21. 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).
20. 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).
19. 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).
18. 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).
17. 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)
15. 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）
14. Chen W., Ishibuchi H., and Shang K. "Modified Distance-based Subset Selection for Evolutionary Multi-objective Optimization Algorithms." IEEE Congress on Evolutionary Computation (CEC2020).
13. Chen W., Ishibuchi H., and Shang K. "Lazy greedy hypervolume subset selection from large candidate solution sets." IEEE Congress on Evolutionary Computation (CEC2020).
12. 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).
11. 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).
10. 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）
9. Ishibuchi H., He L., and Shang K. "Regular Pareto Front Shape is not Realistic." IEEE Congress on Evolutionary Computation (CEC2019).
8. 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）
7. 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).
6. 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).
5. 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).
4. Shang K., et al. “An Event-Driven Based Multiple Scenario Approach for Dynamic and Uncertain UAV Mission Planning.” International Conference on Swarm Intelligence (ICSI2015).
3. 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).
2. 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).
1. Shang K., Feng Z., Ke L. “An ant colony algorithm for permutation flow shop problem.” World Congress on Intelligent Control and Automation (WCICA2012).