| A survey on the hypervolume indicator in evolutionary multiobjective optimization K Shang, H Ishibuchi, L He, LM Pang IEEE Transactions on Evolutionary Computation 25 (1), 1-20, 2020 | 180 | 2020 |
| A double-niched evolutionary algorithm and its behavior on polygon-based problems Y Liu, H Ishibuchi, Y Nojima, N Masuyama, K Shang Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018 | 104 | 2018 |
| A new hypervolume-based evolutionary algorithm for many-objective optimization K Shang, H Ishibuchi IEEE Transactions on Evolutionary Computation 24 (5), 839-852, 2020 | 91 | 2020 |
| R2-based hypervolume contribution approximation K Shang, H Ishibuchi, X Ni IEEE Transactions on Evolutionary Computation, 2019 | 47 | 2019 |
| Regular Pareto front shape is not realistic H Ishibuchi, L He, K Shang 2019 IEEE Congress on Evolutionary Computation (CEC), 2034-2041, 2019 | 41 | 2019 |
| Difficulties in fair performance comparison of multiobjective evolutionary algorithms H Ishibuchi, LM Pang, K Shang Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 39 | 2022 |
| A new R2 indicator for better hypervolume approximation K Shang, H Ishibuchi, ML Zhang, Y Liu Proceedings of the Genetic and Evolutionary Computation Conference, 745-752, 2018 | 38 | 2018 |
| A new framework of evolutionary multi-objective algorithms with an unbounded external archive H Ishibuchi, LM Pang, K Shang Authorea Preprints, 2023 | 34 | 2023 |
| NSGA-II with simple modification works well on a wide variety of many-objective problems LM Pang, H Ishibuchi, K Shang IEEE Access 8, 190240-190250, 2020 | 34 | 2020 |
| A GA-ACO hybrid algorithm for the multi-UAV mission planning problem K Shang, S Karungaru, Z Feng, L Ke, K Terada 2014 14th International Symposium on Communications and Information …, 2014 | 33 | 2014 |
| A multiobjective ACO algorithm for rough feature selection L Ke, Z Feng, Z Xu, K Shang, Y Wang 2010 Second Pacific-Asia Conference on Circuits, Communications and System 1 …, 2010 | 30 | 2010 |
| A scalable multimodal multiobjective test problem H Ishibuchi, Y Peng, K Shang 2019 IEEE congress on evolutionary computation (CEC), 310-317, 2019 | 28 | 2019 |
| Fast greedy subset selection from large candidate solution sets in evolutionary multiobjective optimization W Chen, H Ishibuchi, K Shang IEEE Transactions on Evolutionary Computation 26 (4), 750-764, 2021 | 22 | 2021 |
| Greedy approximated hypervolume subset selection for many-objective optimization K Shang, H Ishibuchi, W Chen Proceedings of the Genetic and Evolutionary Computation Conference, 448-456, 2021 | 15 | 2021 |
| Lazy greedy hypervolume subset selection from large candidate solution sets W Chen, H Ishibuchi, K Shang 2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020 | 14 | 2020 |
| Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms L He, K Shang, H Ishibuchi Applied Soft Computing 92, 106316, 2020 | 13 | 2020 |
| Multi-modal multi-objective optimization: problem analysis and case studies Y Peng, H Ishibuchi, K Shang 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1865-1872, 2019 | 13 | 2019 |
| Clustering-based subset selection in evolutionary multiobjective optimization. ArXiv WY Chen, H Ishibuchi, K Shang arXiv preprint arXiv:2108.08453, 2021 | 12 | 2021 |
| A study of the naïve objective space normalization method in MOEA/D L He, Y Nan, K Shang, H Ishibuchi 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1834-1840, 2019 | 12 | 2019 |
| Benchmarking large-scale subset selection in evolutionary multi-objective optimization K Shang, T Shu, H Ishibuchi, Y Nan, LM Pang Information Sciences 622, 755-770, 2023 | 11 | 2023 |
Hisao IshibuchiSouthern University of Science and TechnologyVerified email at sustech.edu.cn
