Dr Miqing Li

Dr Miqing Li

School of Computer Science

Contact details

School of Computer Science
University of Birmingham
B15 2TT

Dr Miqing Li is a lecturer at School of Computer Science at the University of Birmingham. His research is principally on multi-objective optimisation, where he focuses on developing population-based randomised algorithms (mainly evolutionary algorithms) for both general challenging problems (e.g. many-objective optimisation, constrained optimisation, robust optimisation, expensive optimisation) and specific challenging problems in other fields (e.g. software engineering, system engineering, product disassembly, post-disaster response, neural architecture search, reinforcement learning).

Miqing has published over 60 research papers in scientific journals and international conferences. Some of his papers, since published, have been amongst the most cited papers in corresponding journals such as IEEE Transactions on Evolutionary Computation, Artificial Intelligence, ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Parallel and Distribution Systems

Please follow the link below to find out more about Miqing's work:

Dr Miqing Li - personal web page


  • Algorithms for Data Science (MSc), autumn 2020, module lead.
  • Artificial Intelligence, (BSc, MSc), spring 2020, support.
  • Mathematical Foundation of Artificial Intelligence and Machine Learning (MSc), autumn 2020, support.
  • Artificial Intelligence II (BSc), spring 2021, support.


  • Evolutionary multi-/many-objective optimisation --- algorithm design, performance assessment, archiving.
  • Evolutionary computation for other general challenging scenarios --- constraint handling, multi-modal optimisation, dynamic/robustness optimisation, data-driven optimisation.
  • Multi-criteria decision-making --- visualisation, objective reduction, assisted decision-making.
  • Search-based software engineering --- testing, software product line, software service composition
  • Engineering applications --- disassembly line balancing, post-disaster response, workflow scheduling in cloud computing.
  • Multi-objective optimisation for machine learning --- neural architecture search, reinforcement learning for video game.


Recent publications


Chen, T & Li, M 2022, 'The weights can be harmful: pareto search versus weighted search in multi-objective search-based software engineering', ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/3514233

Liu, Y, Hu, Y, Zhu, N, Li, K, Zou, J & Li, M 2021, 'A decomposition-based multiobjective evolutionary algorithm with weights updated adaptively', Information Sciences, vol. 572, pp. 343-377. https://doi.org/10.1016/j.ins.2021.03.067

Cai, X, Xiao, Y, Li, M, Hu, H, Ishibuchi, H & Li, X 2021, 'A grid-based inverted generational distance for multi/many-objective optimization', IEEE Transactions on Evolutionary Computation, vol. 25, no. 1, 9080110, pp. 21-34. https://doi.org/10.1109/TEVC.2020.2991040

Cai, X, Xiao, Y, Li, Z, Sun, Q, Xu, H, Li, M & Ishibuchi, H 2021, 'A kernel-based indicator for multi/many-objective optimization', IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2021.3105565

Gu, X & Li, M 2021, 'A multi-granularity locally optimal prototype-based approach for classification', Information Sciences, vol. 569, pp. 157-183. https://doi.org/10.1016/j.ins.2021.04.039

Su, Z, Zhang, G, Yue, F, Zhan, D, Li, M, Li, B & Yao, X 2021, 'Enhanced constraint handling for reliability-constrained multiobjective testing resource allocation', IEEE Transactions on Evolutionary Computation, vol. 25, no. 3, 9340399, pp. 537-551. https://doi.org/10.1109/TEVC.2021.3055538

Wang, Z, Luo, T, Li, M, Zhou, JT, Goh, RSM & Zhen, L 2021, 'Evolutionary multi-objective model compression for deep neural networks', IEEE Computational Intelligence Magazine, vol. 16, no. 3, 9492169, pp. 10-21. https://doi.org/10.1109/MCI.2021.3084393

Li, M 2021, 'Is our archiving reliable? Multiobjective archiving methods on “simple” artificial input sequences', ACM Transactions on Evolutionary Learning and Optimization, vol. 1, no. 3, 9, pp. 1-19. https://doi.org/10.1145/3465335

Xiang, Y, Huang, H, Li, M, Li, S & Yang, X 2021, 'Looking for novelty in search-based software product line testing', IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2021.3057853

Liu, Y, Zhu, N & Li, M 2021, 'Solving many-objective optimization problems by a Pareto-based evolutionary algorithm with preprocessing and a penalty mechanism', IEEE Transactions on Cybernetics, vol. 51, no. 11, pp. 5585-5594. https://doi.org/10.1109/TCYB.2020.2988896

Zhang, G, Su, Z, Li, M, Qi, M, Jiang, J & Yao, X 2020, 'A task-oriented heuristic for repairing infeasible solutions on overlapping coalition structure generation', IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 50, no. 3.

Liu, Y, Zhu, N, Li, K, Li, M, Zheng, J & Li, K 2020, 'An angle dominance criterion for evolutionary many-objective optimization', Information Sciences, vol. 509, pp. 376-399. https://doi.org/10.1016/j.ins.2018.12.078

Su, Z, Zhang, G, Yue, F, He, J, Li, M, Li, B & Yao, X 2020, 'Finding the largest successful coalition under the strict goal preferences of agents', ACM Transactions on Autonomous and Adaptive Systems , vol. 14, no. 4.

Conference contribution

Xiang, Y, Huang, H, Zhou, Y, Li, S, Luo, C, Lin, Q, Li, M & Yang, X 2022, Search-based diverse sampling from real-world software product lines. in 2022 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). International Conference on Software Engineering. Proceedings, IEEE, 44th IEEE/ACM International Conference on Software Engineering, Pittsburgh, Pennsylvania, United States, 8/05/22.

Chen, T & Li, M 2021, Multi-objectivizing software configuration tuning. in D Spinellis, G Gousios, M Chechik & M Di Penta (eds), ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM proceedings, Association for Computing Machinery (ACM), pp. 453–465, ESEC/FSE 2021, Athens, Greece, 23/08/21. https://doi.org/10.1145/3468264.3468555

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