Dr Miqing Li

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 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

Teaching

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.

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

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

Chen, P, Chen, T & Li, M 2024, 'MMO: Meta multi-objectivization for software configuration tuning', IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2024.3388910

Gu, Y, Bian, C, Li, M & Qian, C 2024, 'Subset selection for evolutionary multi-objective optimization', IEEE Transactions on Evolutionary Computation, vol. 28, no. 2, pp. 403 - 417. https://doi.org/10.1109/TEVC.2023.3261134

Chen, T & Li, M 2023, 'Do Performance Aspirations Matter for Guiding Software Configuration Tuning? An Empirical Investigation under Dual Performance Objectives', ACM Transactions on Software Engineering and Methodology, vol. 32, no. 3, 68. https://doi.org/10.1145/3571853

Fang, Y, Liu, F, Li, M & Cui, H 2023, 'Domain generalization-based dynamic multiobjective optimization: A case study on disassembly line balancing', IEEE Transactions on Evolutionary Computation, vol. 27, no. 6, pp. 1851 - 1865. https://doi.org/10.1109/TEVC.2022.3233642

Zhou, J, Zhang, Y, Zheng, J & Li, M 2023, 'Domination-Based Selection and Shift-Based Density Estimation for Constrained Multiobjective Optimization', IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 993-1004. https://doi.org/10.1109/TEVC.2022.3190401

Gu, X, Li, M, Shen, L, Tang, G, Ni, Q, Peng, T & Shen, Q 2023, 'Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers', IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1703-1715. https://doi.org/10.1109/TFUZZ.2022.3214241

Zhang, G, Su, Z, Shao, Z, Li, M, Li, B, Yao, X & Li, L 2023, 'New Reliability-Driven Bounds for Architecture-Based Multi-Objective Testing Resource Allocation', IEEE Transactions on Software Engineering, vol. 49, no. 4, pp. 2513-2529. https://doi.org/10.1109/TSE.2022.3223875

Su, Z, Li, M, Zhang, G, Wu, Q, Li, M, Zhang, W & Yao, X 2023, 'Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 5, pp. 4016-4031. https://doi.org/10.1109/TDSC.2022.3215280

Conference contribution

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.

Lyu, H, Herring, D, Wang, L, Ninic, J, Andrews, J, Li, M, Kocvara, M, Spill, F & Wang, S 2024, Multi-Objective Optimization for Flexible Building Space Usage. in 2024 IEEE Conference on Artificial Intelligence (CAI). Artificial Intelligence (CAI), IEEE Conference on, IEEE, 2024 IEEE Conference on Artificial Intelligence , Singapore, 25/07/24.

Liang, Z, Cui, Z & Li, M 2024, Pareto landscape: Visualising the landscape of multi-objective optimisation problems. 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.

Li, M, Han, X & Chu, X 2023, MOEAs Are Stuck in a Different Area at a Time. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 303-311, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583131.3590447

Liang, Z, Li, M & Lehre, PK 2023, Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 383-386, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590646

Bian, C, Zhou, Y, Li, M & Qian, C 2023, Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. in E Elkind (ed.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, pp. 5513-5521, 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023, Macao, China, 19/08/23. https://doi.org/10.24963/ijcai.2023/612

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