| A grid-based evolutionary algorithm for many-objective optimization S Yang, M Li, X Liu, J Zheng IEEE Transactions on Evolutionary Computation 17 (5), 721-736, 2013 | 933 | 2013 |
| Shift-based density estimation for Pareto-based algorithms in many-objective optimization M Li, S Yang, X Liu IEEE Transactions on Evolutionary Computation 18 (3), 348-365, 2014 | 664 | 2014 |
| Evolutionary multi-objective workflow scheduling in cloud Z Zhu, GX Zhang, M Li, X Liu IEEE Transactions on Parallel and Distributed Systems 27 (5), 1344-1357, 2016 | 456 | 2016 |
| A benchmark test suite for evolutionary many-objective optimization R Cheng, M Li, Y Tian, X Zhang, S Yang, Y Jin, X Yao Complex & Intelligent Systems 3, 67-81, 2017 | 447 | 2017 |
| Quality evaluation of solution sets in multiobjective optimisation: A survey M Li, X Yao ACM Computing Surveys 52 (2), 2019 | 419 | 2019 |
| A vector angle based evolutionary algorithm for unconstrained many-objective optimization Y Xiang, Z Yuren, M Li, Z Chen IEEE Transactions on Evolutionary Computation 21 (1), 131-152, 2017 | 403 | 2017 |
| Stable matching-based selection in evolutionary multiobjective optimization K Li, Q Zhang, S Kwong, M Li, R Wang IEEE Transactions on Evolutionary Computation 18 (6), 909-923, 2014 | 379 | 2014 |
| Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization M Li, S Yang, X Liu IEEE Transactions on Evolutionary Computation 20 (5), 645-665, 2016 | 298 | 2016 |
| Bi-goal evolution for many-objective optimization problems M Li, S Yang, X Liu Artificial Intelligence 228, 45-65, 2015 | 249 | 2015 |
| Diversity comparison of Pareto front approximations in many-objective optimization M Li, S Yang, X Liu IEEE Transactions on Cybernetics 44 (12), 2568-2584, 2014 | 185 | 2014 |
| What weights work for you? Adapting weights for any Pareto front shape in decomposition-based evolutionary multi-objective optimisation M Li, X Yao Evolutionary Computation 28 (2), 227-253, 2020 | 155 | 2020 |
| Diversity assessment of multi-objective evolutionary algorithms: Performance metric and benchmark problems Y Tian, R Cheng, X Zhang, M Li, Y Jin IEEE Computational Intelligence Magazine 14 (3), 61-74, 2019 | 153 | 2019 |
| Achieving balance between proximity and diversity in multi-objective evolutionary algorithm K Li, S Kwong, J Cao, M Li, J Zheng, R Shen Information Sciences 182 (1), 220-242, 2012 | 148 | 2012 |
| How to read many-objective solution sets in parallel coordinates M Li, L Zhen, X Yao IEEE Computational Intelligence Magazine 12 (4), 88-97, 2017 | 131 | 2017 |
| SIP: Optimal product selection from feature models using many-objective evolutionary optimisation RM Hierons, M Li, X Liu, S Segura, W Zheng ACM Transactions on Software Engineering and Methodology 25 (2), 2016 | 124 | 2016 |
| Evolutionary multiobjective optimization-based multimodal optimization: Fitness landscape approximation and peak detection R Cheng, M Li, K Li, X Yao IEEE Transactions on Evolutionary Computation 22 (5), 692-706, 2018 | 116 | 2018 |
| Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time Y Fang, H Ming, M Li, Q Liu, DT Pham International Journal of Production Research 58 (3), 846-862, 2020 | 107 | 2020 |
| Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations Y Fang, Q Liu, M Li, Y Laili, DT Pham European Journal of Operational Research 276 (1), 160-174, 2019 | 100 | 2019 |
| How to evaluate solutions in Pareto-based search-based software engineering? A critical review and methodological guidance M Li, T Chen, X Yao IEEE Transactions on Software Engineering 48 (5), 1771-1799, 2022 | 98* | 2022 |
| An angle dominance criterion for evolutionary many-objective optimization Y Liu, N Zhu, K Li, M Li, J Zheng, K Li Information Sciences 509, 376-399, 2020 | 97 | 2020 |
