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


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

Hierons, RM, Li, M, Liu, X, Parejo, JA, Segura, S & Yao, X 2020, 'Many-objective test suite generation for software product lines', ACM Transactions on Software Engineering and Methodology, vol. 29, no. 1, 2. https://doi.org/10.1145/3361146

Li, M & Yao, X 2020, 'What weights work for you? Adapting weights for any pareto front shape in decomposition-based evolutionary multiobjective optimisation', Evolutionary Computation, vol. 28, no. 2, pp. 227-253. https://doi.org/10.1162/evco_a_00269

Fang, Y, Ming, H, Li, M, Liu, Q & Pham, DT 2019, 'Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time', International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1602290

Li, M & Yao, X 2019, 'Quality evaluation of solution sets in multiobjective optimisation: a survey', ACM Computing Surveys, vol. 52, no. 2, 26. https://doi.org/10.1145/3300148

Cheng, R, Li, M, Li, K & Yao, X 2018, 'Evolutionary Multiobjective Optimization Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection', IEEE Transactions on Evolutionary Computation, vol. 22, no. 5, pp. 692 - 706. https://doi.org/10.1109/TEVC.2017.2744328

Fang, Y, Liu, Q, Li, M, Laili, Y & Pham, DT 2018, 'Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations', European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.12.035

Li, M, Grosan, C, Yang, S, Liu, X & Yao, X 2018, 'Multi-line distance minimization: a visualized many-objective test problem suite', IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 61-78. https://doi.org/10.1109/TEVC.2017.2655451

Zhang, G, Su, Z, Li, M, Qi, M, Jiang, J & Yao, X 2017, 'A task-oriented heuristic for repairing infeasible solutions to overlapping coalition structure generation', IEEE Transactions on Systems, Man and Cybernetics: Systems, pp. 1-17. https://doi.org/10.1109/TSMC.2017.2712624

Yu, G, Shen, R, Zheng, J, Li, M, Zou, J & Liu, Y 2017, 'Binary search based boundary elimination selection in many-objective evolutionary optimization', Applied Soft Computing, vol. 60, pp. 689-705. https://doi.org/10.1016/j.asoc.2017.07.030

Zhang, G, Su, Z, Li, M, Yue, F, Jiang, J & Yao, X 2017, 'Constraint handling in NSGA-II for solving optimal testing resource allocation problems', IEEE Transactions on Reliability, vol. 66, no. 4, pp. 1193-1212. https://doi.org/10.1109/TR.2017.2738660

Li, M, Zhen, L & Yao, X 2017, 'How to read many-objective solution sets in parallel coordinates [educational forum]', IEEE Computational Intelligence Magazine, vol. 12, no. 4, pp. 88-100. https://doi.org/10.1109/MCI.2017.2742869

Conference contribution

Li, M & Yao, X 2019, An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. in K Deb, E Goodman, C Coello Coello, K Klamroth, K Miettinen, S Mostaghim & P Reed (eds), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Lecture Notes in Computer Science - Theoretical Computer Science and General Issues, vol. 11411, Springer, pp. 15-26, 10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19), East Lansing, Michigan, United States, 10/03/19. https://doi.org/10.1007/978-3-030-12598-1_2

Li, M, Chen, T & Yao, X 2018, A critical review of "a practical guide to select quality indicators for assessing pareto-based search algorithms in search-based software engineering": Essay on quality indicator selection for SBSE. in ICSE-NIER 2018 Proceedings of the 2018 ACM/IEEE 40th International Conference on Software Engineering: New Ideas and Emerging Results. Proceedings - International Conference on Software Engineering, Association for Computing Machinery (ACM), pp. 17-20, 40th ACM/IEEE International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2018, Gothenburg, Sweden, 30/05/18. https://doi.org/10.1145/3183399.3183405, https://doi.org/https://dl.acm.org/citation.cfm?doid=3183399.3183405

Zhen, L, Li, M, Cheng, R, Peng, D & Yao, X 2017, Adjusting parallel coordinates for investigating multi-objective search. in X Li, M Zhang, Q Zhang, M Middendorf, KC Tan, Y Tan, Y Jin, Y Shi & K Tang (eds), Simulated Evolution and Learning : 11th International Conference, SEAL 2017, Proceedings. Lecture Notes in Computer Science, vol. 10593 , Springer, pp. 224-235, 11th International Conference on Simulated Evolution and Learning, SEAL 2017, Shenzhen, China, 10/11/17. https://doi.org/10.1007/978-3-319-68759-9_19

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