Dr Miqing Li BSc, MSc, PhD

School of Computer Science
Associate Professor

Contact details

Telephone: +44 (0) 121 414 3712
Email: m.li.8@bham.ac.uk

Address: School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Miqing Li is a computer science researcher exploring how AI can be used to solve complex optimisation problems, such as multi-/many-objective, expensive, uncertain, and combinatorial problems.

Biography

Dr Miqing Li’s research lies at the interface of AI and optimisation, focusing on the development of computational intelligence techniques (e.g., evolutionary algorithms, local search, Bayesian optimisation) to tackle complex fundamental and practical optimisation problems, particularly those involving multiple objectives.

Miqing’s current research interests include:

Multi-objective combinatorial optimisation – evolutionary algorithms, local search and neural combinatorial optimisation.
Expensive and robust optimisation - evolutionary algorithms and Bayesian optimisation.
Fundamental issues in multi-objective optimisation, including algorithm design, archiving, fitness landscape analysis, performance assessment, visualisation, multi-criteria decision-making, and many-objective optimisation.
Practical optimisation applications in various areas, including software engineering, machine learning, mechanical engineering, and chemical engineering.

Please see Dr Miqing Li’s personal web page to find out more about his work.

Teaching

Algorithms for Data Science (module lead)
Artificial Intelligence 2

Postgraduate supervision

The areas I am interested in supervising include: multi-objective optimisation, combinatorial optimisation, evolutionary algorithms, local search, Bayesian optimisation, neural combinatorial optimisation, multi-criteria decision-making, and optimisation in applied areas.

Research

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.

Publications

Recent publications

Article

Tong, H, Li, M, Liu, J & Yao, X 2025, 'How do dynamic events change the fitness landscape of traveling salesman problems?', IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2025.3538547

Jiang, C & Li, M 2025, 'Multi-Objectivising Acquisition Functions in Bayesian Optimisation', ACM Transactions on Evolutionary Learning and Optimization. https://doi.org/10.1145/3716504

Zhang, Q, Fu, X, Yang, S, Jiang, S, Li, M & Zheng, Z 2025, 'Solving dynamic multi-objective engineering design problems via fuzzy c-means prediction algorithm', Swarm and Evolutionary Computation, vol. 98, 102057. https://doi.org/10.1016/j.swevo.2025.102057

Bian, C, Zhou, Y, Li, M & Qian, C 2025, 'Stochastic population update can provably be helpful in multi-objective evolutionary algorithms', Artificial Intelligence, vol. 341, 104308. https://doi.org/10.1016/j.artint.2025.104308

Yu, Z, Zhou, W, Tang, C, Li, M, Zhen, L & Lv, J 2026, 'UniQ-ViT: Optimization-driven uniform quantization for vision transformer acceleration', Neurocomputing, vol. 663, 132072. https://doi.org/10.1016/j.neucom.2025.132072

Chen, T & Li, M 2024, 'Adapting Multi-objectivized Software Configuration Tuning', Proceedings of the ACM on Software Engineering, vol. 1, no. FSE, 25, pp. 539-561. https://doi.org/10.1145/3643751

Han, X, Chao, T, Yang, M & Li, M 2024, 'A steady-state weight adaptation method for decomposition-based evolutionary multi-objective optimisation', Swarm and Evolutionary Computation, vol. 89, 101641. https://doi.org/10.1016/j.swevo.2024.101641

Xiang, Y, Huang, H, Li, S, Li, M, Luo, C & Yang, X 2024, 'Automated test suite generation for software product lines based on quality-diversity optimisation', ACM Transactions on Software Engineering and Methodology, vol. 33, no. 2, 46, pp. 1–52. https://doi.org/10.1145/3628158

Conference contribution

Ye, Y, Chen, T & Li, M 2025, Distilled Lifelong Self-Adaptation for Configurable Systems. in 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). Proceedings - International Conference on Software Engineering, IEEE, pp. 1333-1345, 47th International Conference on Software Engineering, Ottowa, Ontario, Canada, 26/04/25. https://doi.org/10.1109/ICSE55347.2025.00094

Zhang, Q, Li, M, Tang, K & Yao, X 2025, When is non-deteriorating population update in MOEAs beneficial? in H Singh, T Ray, J Knowles, X Li, J Branke, B Wang & A Oyama (eds), Evolutionary Multi-Criterion Optimization: 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part I. 1 edn, Lecture Notes in Computer Science, vol. 15512, Springer, pp. 31-45, 13th International Conference on Evolutionary Multi-Criterion Optimization , Canberra, Australia, 4/03/25. https://doi.org/10.1007/978-981-96-3538-2_3

Cui, Z, Liang, Z, Pang, LM, Ishibuchi, H & Li, M 2025, When to Truncate the Archive? On the Effect of the Truncation Frequency in Multi-Objective Optimisation. in GECCO '25: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), The Genetic and Evolutionary Computation Conference 2025, Málaga, Spain, 14/07/25.

Ren, S, Bian, C, Li, M & Qian, C 2024, A first running time analysis of the strength Pareto evolutionary algorithm 2 (SPEA2). in Parallel Problem Solving from Nature – PPSN XVIII. Lecture Notes in Computer Science, Springer, 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenberg, Austria, 14/09/24.

Bian, C, Ren, S, Li, M & Qian, C 2024, An archive can bring provable speed-ups in multi-objective evolutionary algorithms. in K Larson (ed.), IJCAI '24: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence., 763, Proceedings of the International Joint Conference on Artificial Intelligence, Association for Computing Machinery (ACM), pp. 6905-6913, 33rd International Joint Conference on Artificial Intelligence, Jeju, Korea, Republic of, 3/08/24. https://doi.org/10.24963/ijcai.2024/763

Li, M, Han, X, Chun, X & Liang, Z 2024, Empirical Comparison between MOEAs and Local Search on Multi-Objective Combinatorial Optimisation Problems. in GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), pp. 547-556, GECCO '24: Genetic and Evolutionary Computation Conference, Melbourne, Victoria, Australia, 14/07/24. https://doi.org/10.1145/3638529.3654077

Paper

Tan, Z, Yuan, B, Wang, H & Li, M 2024, 'A new step size update strategy for CMA-ES in multi-objective optimisation', Paper presented at Biennial International Conference on Artificial Evolution, Talence, France, 29/10/24 - 31/10/24.

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