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, focusing on computational intelligence methods (e.g., evolutionary algorithms and Bayesian optimisation) optimisation, with a primary emphasis on multi-objective problems. My current research interests include:

Multi-objective combinatorial optimisation (EAs, local search, NCO);
Expensive and robust optimisation (EAs, BO);
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

June 2026 - Our work on visually analysing the fitness landscape of dynamic multi-objective optimisation problems and algorithms has been accepted by IEEE Transactions on Evolutionary Computation. Congratulations to Jose Gustavo and Leandro!
June 2026 - Our work (collaborated with Dr Jingyu Huang) on arguing that it doesn't need to find the entire Pareto front in many-objective Bayesian optimisation has been accepted by Transactions of Machine Learning Research. Congratulations to Chao and Jingyu!
June 2026 - Our work on designing EA-based optimisation framework for multi-fidelity software configurable systems has been accepted by ASE'26. Congratulations to Yulong and Tao!
June 2026 - I am delighted to share that Daniel Fryer received the Best Presentation Award at the School.
May 2026 - Our work on developing a new multi-objective CMA-ES (which outperforms "best 2016" on a certain class of problems) has been accepted by PPSN'26. Congratulations to Zheng and Bo!
May 2026 - I am delighted to share I have been nominated for the 2026 Awards for Excellence in Doctoral Research Supervision at the University.
April 2026 - Our work (collaborated with Prof Chao Qian's group) on developing an MOEA with provable benefits, in which one population focuses on exploration and one population on exploitation has been accepted by IJCAI'26. Congratulations to Chenglin, Shengjie, Zimin and Chao!
April 2026 - Our work (collaborated with Prof Chao Qian's group) on constructing a class of integer multi-objective functions for theoretical analysis has been accepted by IJCAI'26. Congratulations to Yuetong, Zeqiong, Shengjie, Zimin and Chao!
April 2026 - Our work on visualising the landscape of multi-objective problems (an extension of our PPSN'24 work) has been accepted by Mathematical and Computational Applications. Congratulations to Zhiji and Zimin! Thanks for the invitation from Prof Oliver Schütze.
March 2026 - Our work (collaborated with Dr Yi Xiang and colleagues) on enhancing the diversity of the sampled configurations in pairwise testing has been accepted by FSE'26. Congratulations to Yi and colleagues!
March 2026 - Collaborated with Prof Changhe Li's group, three papers have been accepted by GECCO'26, with one receiving the Best Paper Award Nomination. Congratulations to Change's group!
March 2026 - Our work on investigating the scalability of EAs and local search on multi-objective combinatorial optimisation problems has been accepted by GECCO'26. Congratulations to Menghao and Zimin!
March 2026 - Our work on studying the connection between fitness landscapes and domain knowledge in software configuration tuning has been accepted by ACM Transactions on Software Engineering and Methodology. Congratulations to Yulong, Hongyuan, Chao, Tao!
Jan 2026 - I am pleased to share the news that Zimin Liang has successfully defended his PhD viva. Many congratulations, well done Zimin!

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]