Miqing Li

I am an Associate Professor in the School of Computer Science at the University of Birmingham, UK. My research sits at the intersection between AI and optimisation, where I am interested in developing computational intelligence techniques (e.g., evolutionary algorithms) to solve both fundamental and practical optimisation problems. My current research interests include:

Multi-objective combinatorial optimisation (EAs, local search, NCO);
Expensive and robust optimisation (EAs, Bayesian);
General multi-objective optimisation issues (e.g. fitness landscape analysis and visualisation, archiving, performance assessment);
Many-objective optimisation (algorithm design, visualisation, assisted decision-making);
Practical applications (e.g. in software engineering, mechanical engineering, chemical engineering).

This website is my research portfolio where you can find resources of research projects I work on. This includes brief descriptions (ideas and results) of some of my projects on basic research and applied research and their resulting papers (with code and data). You are very welcome to contact me if you are interested in pursuing a PhD on any of the relevant topics mentioned above.

Recent News

April 2025 - Our work (collaborated with Prof Chao Qian's group) on using an archive can bring provable speed-ups for stochastic population update in MOEAs has been accepted by IJCAI'25. Congratulations to Shengjie, Zimin and Chao!
March 2025 - Our work (collaborated with Dr Zhiwei Xu) on solving the multi-objective shortest path problem has been accepted by IEEE Transactions on Intelligent Transportation Systems . Congratulations to Zhiwei and colleagues!
March 2025 - Our work (collaborated with Prof Hisao Ishibuchi's group) on investigating the frequency of maintaining the archive in multi-objective optimisation has been accepted by GECCO'25. Congratulations to Zhiji, Zimin, Lie Meng and Hisao!
March 2025 - Our work (collaborated with Prof Guofu Zhang's group) on solving overlapping coalition structure generation in multi-agent systems has been accepted by Journal of Artificial Intelligence Research. Congratulations to Guofu and colleagues!
March 2025 - I am delighted to share that our work on investigating when non-deteriorating population update in MOEAs beneficial has received the Outstanding Student Paper Award at EMO'25. Congratulations to Qiaozhi, Ke and Xin!
February 2025 - Our work (collaborated with Prof Chao Qian's group) of analytically showing stochastic population update in MOEAs can be helpful has been accepted by Artificial Intelligence. Congratulations to Chao, Yawen and Chao!
January 2025 - Our work on multi-objectivising acquisition functions in Bayesian optimisation has been accepted by ACM Transactions on Evolutionary Learning and Optimization. Congratulations to Chao!
January 2025 - Our work (collaborated with Dr Hao Tong) on investigating the fitness landscape of TSP under dynamic events has been accepted by IEEE Transactions on Evolutionary Computation. Congratulations to Hao, Jialin and Xin!

Highlights

[PPSN24] develops a tool to visualise the landscape of multi-objective optimisation problems with a low-dimensional decision space.

[IJCAI24] analytically shows that, for the first time, using an archive can guarantee speed-ups for MOEAs. This means that with an archive, the size of the population can reduce to a small constant. Therefore, there is no need for the population to keep all the Pareto optimal solutions found, which is a practice considered in all existing theoretical studies. [PDF]

[GECCO24] found that 1) MOEAs can compete with local search in combinatorial problems, and 2) a very simple search heuristic, SEMO, performs better than well-established MOEAs (NSGA-II, MOEA/D and SMS-EMOA) and local search algorithms (PLS and anytime PLS) [PDF]

[TEVC23] A systematic survey of archiving methods in multi-objective optimisation from a general theoretical perspective, in which four classes of archiving methods are identified. We also present that archiving methods based on weakly Pareto compliant indicators (e.g., ε-indicator, R2, IGD+) can achieve the same theoretical desirables as archiving methods based on Pareto compliant indicators (e.g., hypervolume indicator).

[IJCAI23] theoretically proved that introducing randomness into the population update process in MOEAs can be beneficial. This finding is against all mainstream MOEAs, challenging their greedy, deterministic population update mechanisms during the search [PDF]

[GECCO23] found that MOEAs are stuck in a different area at a time; but this only happens to combinatorial problems, but not continuous problems [PDF]

[NeurIPS22] A multi-agent dynamic algorithm configuration, with one agent working for one type of configuration hyperparameter, and its instantiation to dynamically configure MOEA/D (e.g., the weights, neighbourhood size, etc) during the search. [PDF]

[TOSEM22] Converting a multi-objective optimisation problem into a single-objective one may not be a good option even if clear preferences between the objectives exist. [Read More]

[TELO21] Through a set of artificial sequences, this work refutes a common belief and shows Pareto-based, indicator-based and decomposition-based criteria may fail on extremely simple Pareto front shapes (i.e. 1D/2D simplex). [Read More]

[FSE21] A bi-objective model to optimise a performance attribute (single-objective) in the software configuration tuning task - a black-box, expensive optimisation problem with rugged landscape and numerous local optima. [Read More]

[TSE20] A critical review and methodological guidance of how to select and use evaluation methods in different multi-objective SBSE scenarios. [Read More]

[ECJ20] An adaptive weight update approach that enables the decomposition-based EMO method to work well for any Pareto front shape. [PDF] [Read More]

[TOSEM20] A customised evolution strategy to generate test suites for software product lines through simultaneously optimising 9 objectives relating to test selection and test prioritisation. [PDF] [Read More]

[CSUR19] A survey of 100 quality indicators in multi-objective optimisation, providing guidance on how to select and use quality indicators in various situations. [PDF] [Read More]

[TEVC18, CEC14] An award-winning work that designs a test problem suite to aid the visual examination of algorithm performance in many-objective optimisation. [PDF1] [PDF2] [Read More]