Papers
To appear
X. Han, T. Chao, M. Yang, M. Li A steady-state weight adaptation method for decomposition-based evolutionary multi-objective optimisation. Swarm and Evolutionary Computation, 2024, accepted.
Z. Liang, Z. Cui, M. Li. Pareto landscape: Visualising the landscape of multi-objective optimisation problems. In International Conference on Parallel Problem Solving from Nature (PPSN), 2024, accepted.Â
S. Ren, C. Qian, C. Bian, M. Li. A first running time analysis of the strength Pareto evolutionary algorithm 2 (SPEA2). In International Conference on Parallel Problem Solving from Nature (PPSN), 2024, accepted.Â
C. Bian, S. Ren, M. Li, and C. Qian. An archive can bring provable speed-ups in multi-objective evolutionary algorithms. In International Joint Conference on Artificial Intelligence (IJCAI), 2024, accepted.
S. Ren, Z. Qiu, C. Bian, M. Li, and C. Qian. Maintaining diversity provably helps in evolutionary multimodal optimization. In International Joint Conference on Artificial Intelligence (IJCAI), 2024, accepted.
P. Chen, T. Chen, M. Li. MMO: Meta multi-objectivization for software configuration tuning. IEEE Transactions on Software Engineering, 2024, accepted.
M. Li, X. Hang, X. Chu, Z. Liang. Empirical comparison between MOEAs and local search on multi-objective combinatorial optimisation problems. In Genetic and Evolutionary Computation Conference (GECCO), 2024, accepted. [PDF]
H. Lyu, D. Herring, L. Wang, J. Ninic, J. Andrews, M. Li, M. Kocvara, F. Spill, and S. Wang. Multi-objective optimization for flexible building space usage. In IEEE Conference on Artificial Intelligence (CAI), 2024, accepted.Â
G. Zhang, L. Li, Z. Su, F. Yue, Y. Chen, M. Li, X. Yao. On estimating the feasible solution space of multi-objective testing resource allocation. ACM Transactions on Software Engineering and Methodology, 2024, accepted.Â
T. Chen, M. Li. Adapting multi-objectivized software configuration tuning. ACM International Conference on the Foundations of Software Engineering (FSE), 2024, accepted.Â
2024
M. Li, M. López-Ibáñez, X. Yao. Multi-objective archiving.  IEEE Transactions on Evolutionary Computation, 28(3), 696-717, 2024.
Y. Wang, L. Zhen, J. Zhang, M. Li, L. Zhang, Z. Wang, et al. MedNAS: Multi-scale training-free neural architecture search for medical image analysis. IEEE Transactions on Evolutionary Computation, 28(3), 668 - 681, 2024.
Y. Gu, C. Bian, M. Li, C. Qian. Subset selection for evolutionary multi-objective optimization. IEEE Transactions on Evolutionary Computation, 28(2), 403-417, 2024.Â
Y. Xiang, H. Huang, S. Li, M. Li, C. Luo, X. Yang. Automated test suite generation for software product lines based on quality-diversity optimisation. ACM Transactions on Software Engineering and Methodology, 33(2), 1-52, 2024. Â
2023
T. Chen, M. Li. The weights can be harmful: Pareto search versus weighted search in multi-objective search-based software engineering. ACM Transactions on Software Engineering and Methodology, 32(1), 1-40, 2023. [PDF] [Read More]
X. Gu, M. Li, L. Shen, G. Tang, Q. Ni, T. Peng, Q. Shen. Multi-objective evolutionary optimisation for prototype-based fuzzy classifiers. IEEE Transactions on Fuzzy Systems, 31(5), 1703-1715, 2023.
T. Chen, M. Li. Do performance aspirations matter for guiding software configuration tuning? An empirical investigation under dual performance objectives. ACM Transactions on Software Engineering and Methodology, 32(3), 1-41, 2023. [PDF]
Y. Fang, F, Liu, M. Li, H. Cui. Domain generalization-based dynamic multiobjective optimization: A case study on disassembly line balancing. IEEE Transactions on Evolutionary Computation, 27(6), 1851 - 1865, 2023.Â
G. Zhang, L. Li, Z. Su, Z. Shao, M. Li, B. Li, X. Yao. New reliability-driven bounds for architecture-based multi-objective testing resource allocation.  IEEE Transactions on Software Engineering,  49(4), 2513-2529, 2023. [PDF]
J. Zhou, Y. Zhang, J. Zheng, M. Li. Domination-based selection and shift-based density estimation for constrained multiobjective optimization. IEEE Transactions on Evolutionary Computation, 27(4), 993 - 1004, 2023.Â
Z. Su, M. Li, G. Zhang, Q. Wu, M. Li, W. Zhang, X. Yao. Robust audio copy-move forgery detection using constant Q spectral sketches and GA-SVM. Â IEEE Transactions on Dependable and Secure Computing, Â 20(5), 4016 - 4031, 2023. [PDF]
M. Li, X. Han, X. Chu. MOEAs are stuck in a different area at a time. In Genetic and Evolutionary Computation Conference (GECCO), 303-311, 2023. [PDF]
Z. Liang, M. Li, P. K. Lehre. Non-elitist evolutionary multi-objective optimisation: Proof-of-principle results. In Genetic and Evolutionary Computation Conference (GECCO) Companion, 383-386, 2023. [PDF]
C. Bian, Y. Zhou, M. Li, C. Qian. Stochastic population update can provably be helpful in multi-objective evolutionary algorithms. In 32nd International Joint Conference on Artificial Intelligence (IJCAI), 5513-5521, 2023. [PDF]
2022
M. Li, T. Chen, X. Yao. How to evaluate solutions in Pareto-based search-based software engineering? A critical review and methodological guidance. IEEE Transactions on Software Engineering, 48(5): 1771-1799, 2022. [PDF] [Read More]
Y. Xie, M. Li, X Liu. An effective and efficient evolutionary algorithm for many-objective optimization. Information Science, 617, 211-233, 2022. [PDF]
Y Xiang, X Yang, H Huang, Z Huang, M Li Sampling configurations from software product lines via probability-aware diversification and SAT solving. Automated Software Engineering, 29 (2), 1-45, 2022.Â
Y. Xiang, H. Huang, M. Li, S. Li, X Yang. Looking for novelty in search-based software product line testing. IEEE Transactions on Software Engineering, 48(7): 2317-2338, 2022. [PDF]
X. Cai, Y. Xiao, Z. Li, Q. Sun, H. Xue, M. Li, H. Ishibuchi. A kernel-based indicator for multi/many-objective optimization. IEEE Transactions on Evolutionary Computation, 26(4): 602-615, 2022. [PDF] [Matlab code]Â
W. Luo, L. Shi, X. Lin, J. Zhang, M. Li, X. Yao, Finding top-k solutions for the decision-maker in multiobjective optimization. Information Science, 613, 204-227, 2022.
J. Zhang, M. Li, W. Liu, S. Lauria, X. Liu. Many-objective optimization meets recommendation systems: A food recommendation scenario. Neurocomputing, 503: 109-117.
K. Xue, J. Xu, L. Yuan, M Li, C. Qian, Z. Zhang, Y. Yu. Multi-agent dynamic algorithm configuration. In Advances in Neural Information Processing Systems (NeurIPS), 2022. [PDF]
Y. Xue, M. Li, H. Arabnejad, D. Suleimenova, A. Jahani, B. C. Geiger, Z. Wang, X. Liu, D. Groen. Camp location selection in humanitarian logistics: A multiobjective simulation optimization approach. International Conference on Computational Science (ICCS), 497-504, 2022.
Y. Xiang, H. Huang, Y. Zhou, S. Li, C. Luo, Q. Lin, M. Li, and X. Yang. Search-based diverse sampling from real-world software product lines. In 44th IEEE/ACM International Conference on Software Engineering (ICSE), 1945–1957, 2022. [PDF]Â
2021
M. Li. Is our archiving reliable? Multiobjective archiving methods on “simple” artificial input sequences. ACM Transactions on Evolutionary Learning and Optimization, 1(3), 2021. [PDF] [Sequences] [Read More]
Z. Wang, T. Luo, M. Li, J. T. Zhou, R. S. M. Goh, L. Zhen. Evolutionary Multi-Objective Model Compression. IEEE Computational Intelligence Magazine, 16(3), 2021. [PDF]
Y. Liu, N. Zhu, M. Li. Solving many-objective optimization problems by a Pareto-based evolutionary algorithm with preprocessing and a penalty mechanism. IEEE Transactions on Cybernetics, 51(11), 2021. [PDF]
Z. Su, G. Zhang, F. Yue, D. Zhan, M. Li, B. Li, X. Yao. Enhanced constraint handling for reliability-constrained multi-objective testing resource allocation. IEEE Transactions on Evolutionary Computation, 25(3), 2021. [PDF] [Code and parameters]Â
Y Liu, Y Hu, N Zhu, K Li, J Zou, M Li. A decomposition-based multiobjective evolutionary algorithm with weights updated adaptively. Information Sciences, 572, 343-377, 2021. [PDF]
X Gu, M Li. A multi-granularity locally optimal prototype-based approach for classification. Information Sciences, 569, 157-183, 2021.
X. Cai, H. Hu, M. Li, Y. Xiao, H. Ishibuchi, X. Li. A grid-based inverted generational distance for multi/many-objective optimization. IEEE Transactions on Evolutionary Computation, 25(1), 2021. [PDF] [Matlab code]
T. Chen and M. Li. Multi-objectivizing software configuration tuning. The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 453–465, 2021. [PDF] [Java code] [Read More]
2020
M. Li and X. Yao, What weights work for you? Adapting weights for any Pareto front shape in decomposition-based evolutionary multi-objective optimisation. Evolutionary Computation, 28(2), 2020. [PDF] [C code] [Read More]
R. M. Hierons, M. Li, X. Liu, J. A. Parejo, S. Segura, X. Yao. Many-objective test suite generation for software product lines. ACM Transactions on Software Engineering and Methodology, 29(1), 2020. [C code] [Read More]
Z. Su, G. Zhang, F. Yue, J. He, M. Li, B. Li, X. Yao. Finding the largest successful coalition under the strict goal preferences of agents. ACM Transactions on Autonomous and Adaptive Systems, 14(4), 2020. [PDF] [C++ code and test instances]Â
L. Zhen, M. Li, D. Peng, X. Yao. Objective reduction for visualising many-objective solution sets. Information Sciences, 512: 278-294, 2020. [Matlab code]
Y. Fang, H. Ming, M. Li, Q. Liu, D. T. Pham. Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. International Journal of Production Research, 58(3): 846-862, 2020.
Y. Xiang, X. Yang, Y. Zhou, Z. Zheng, M. Li, H. Huang. Going deeper with optimal software products selection using many-objective optimization and satisfiability solvers. Empirical Software Engineering, 25: 591-626, 2020.
Y. Liu, N. Zhu, K. Li, M. Li, J. Zheng, K. Li. An angle dominance criterion for evolutionary many-objective optimization. Information Sciences, 509: 376-399, 2020.
G. Zhang, Z. Su, M. Li, M. Qi, J. Jiang, and X. Yao. A task-oriented heuristic for repairing infeasible solutions on overlapping coalition structure generation. IEEE Transactions on Systems, Man and Cybernetics: Systems, 50(3), 785-801, 2020. [PDF] [test samples]
W. Liu, W. Luo, X. Lin, M. Li, and S. Yang. An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions. IEEE Congress on Evolutionary Computation (CEC), 2020.Â
Y. Xue, M. Li, and X. Liu. Angle-based crowding degree estimation for many-objective optimization. International Symposium on Intelligent Data Analysis (IDA), 574-586, 2020.
2019
M. Li and X. Yao, Quality evaluation of solution sets in multiobjective optimisation: A survey. ACM Computing Surveys, 52(2), 2019. [PDF] [Read More]
T. Chen, M. Li, X. Yao. Standing on the shoulders of giants: seeding search-based multi-objective optimization with prior knowledge for software service composition. Information and Software Technology, 114: 155-175, 2019.
Y. Xue, M. Li, M. Shepperd, S. Lauria, X. Liu. A novel aggregation-based dominance for Pareto-based evolutionary algorithms to configure software product lines. Neurocomputing, 364: 32-48, 2019.
Y. Tian, R. Cheng, X. Zhang, M. Li, Y. Jin. Diversity assessment of multi-Objective evolutionary algorithms: performance metric and benchmark problems. IEEE Computational Intelligence Magazine, 14(3): 61-74, 2019.
Y. Fang, Q. Liu, M. Li. Y. Laili, D. T. Pham, Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. European Journal of Operational Research, 276: 160-174, 2019.
M. Li and X. Yao. An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. The 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), 15-26, 2019. [PDF]
2018
M. Li, C. Grosan, S. Yang, X. Liu, X. Yao, Multi-line distance minimization: A visualized many-objective test problem suite. IEEE Transactions on Evolutionary Computation, 22(1): 61-78, 2018. [PDF] [supplement] [C code] [Pareto front & Pareto set] [Read More]
Y. Xiang, Y. Zhou, Z. Zheng, M. Li, Configuring software product lines by combining many-objective optimization and SAT solvers. ACM Transactions on Software Engineering and Methodology, 26(4): 2018. [Java code]
R. Cheng, M. Li, K. Li, X. Yao. Evolutionary multiobjective optimization based multimodal optimization: fitness landscape approximation and peak detection. IEEE Transactions on Evolutionary Computation, 22(5): 692-706, 2018. [supplement] [Matlab code]
K. Wang, X. Li, C. Jia, S. Yang, M. Li, Y. Li, Multiobjective optimization of the production process for ground granulated blast furnace slags. Soft Computing, 22(24): 8177-8186, 2018.
T. Chen, M. Li, X. Yao. On the effects of seeding strategies: A case for search-based multi-objective service composition. The Genetic and Evolutionary Computation Conference (GECCO), 1419-1426, 2018.
M. Li, T. Chen, X. Yao. 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. The 40th International Conference on Software Engineering (ICSE): New Ideas and Emerging Results Track, 17-20, 2018. [PDF]
L. Zhen, M. Li, R. Cheng, D. Peng, X. Yao. Multiobjective Test Problems with Degenerate Pareto Fronts. arXiv preprint arXiv:1806.02706. [Matlab code]
2017
M. Li, L. Zhen, X. Yao. How to read many-objective solution sets in parallel coordinates. IEEE Computational Intelligence Magazine, 12(4): 88-97, 2017. [PDF] [data]
G. Zhang, Z. Su, M. Li, F. Yue, J. Jiang, X. Yao. A Constraint handling based NSGA-II for solving optimal testing resource allocation problems. IEEE Transactions on Reliability, 66(4): 1193-1212, 2017. [PDF] [test samples] [C++ code]
Y. Xiang, Y. Zhou, M. Li, Z. Chen. A vector angle based evolutionary algorithm for unconstrained many-objective optimization. IEEE Transactions on Evolutionary Computation, 21(1): 131-152, 2017. [Java code]
Y. Xiang, J. Peng, Y. Zhou, M. Li, Z. Chen. An angle based constrained many-objective evolutionary algorithm. Applied Intelligence, 47(3): 705-720, 2017.
R. Shen, J. Zheng, M. Li, J. Zou. Many-objective optimization based on information separation and neighbor punishment selection. Soft Computing, 21(5): 1109-1128, 2017.
G. Yu, R. Shen, J. Zheng, M. Li, J. Zou, Y. Liu. Binary search based boundary elimination selection in many-objective evolutionary optimization. Applied Soft Computing, 60: 689-705, 2017.
R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, X. Yao A benchmark test suite for evolutionary many-objective optimization. Complex & Intelligent Systems, 3(1): 67-81, 2017.
L. Zhen, M. Li, R. Cheng, D. Peng, X. Yao. Adjusting parallel coordinates for investigating multi-objective search. The 11th International Conference on Simulated Evolution and Learning (SEAL), 224-235, 2017. (Best Student Paper Award). [PDF] [Matlab code]
R. Cheng, M. Li and X. Yao. A visualization method for benchmark studies of multimodal optimization. IEEE Congress on Evolutionary Computation (CEC), 263-270, 2017.
M. Li, X. Yao. Dominance move: A measure of comparing solution sets in multiobjective optimization. arXiv preprint arXiv:1702.00477.
2016
M. Li, S. Yang, and X. Liu. Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5): 645-665, 2016. [PDF] [C code] [Read More]
R. M. Hierons, M. Li, X. Liu, S. Segura, and W. Zheng. SIP: Optimal product selection from feature models using many-objective evolutionary optimisation. ACM Transactions on Software Engineering and Methodology, 25(3), 2016. [C code] [data]
Z. Zhu, G. Zhang, M. Li, and X. Liu. Evolutionary multi-objective workflow scheduling in cloud. IEEE Transactions on Parallel and Distribution Systems, 27(5): 1344-1357, 2016.
G. Yu, J. Zheng, R. Shen, and M. Li. Decomposing the user preference in multiobjective optimization. Soft Computing, 20(10): 4005-4021, 2016.
W. Zheng, R. M. Hierons, M. Li, X. Liu, and V. Vinciotti. Multi-objective optimisation for regression testing. Information Sciences, 334-335: 1-16, 2016. [PDF]
S. Jiang, S. Yang and M. Li. On the use of hypervolume for diversity measurement of Pareto front approximations. IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2016. [PDF]
2015
M. Li. Evolutionary Many-Objective Optimisation: Pushing the Boundaries. PhD Dissertation, Brunel University London, UK, December 2015. [PDF]
M. Li, S. Yang, and X. Liu. Bi-goal evolution for many-objective optimization problems. Artificial Intelligence, 228: 45-65, 2015. [PDF] [C code]
M. Li, S. Yang, and X. Liu. A performance comparison indicator for Pareto front approximations in many-objective optimization. The Genetic and Evolutionary Computation Conference (GECCO), 703-710, 2015. [PDF] [C code]
J. Zheng, H. Bai, R. Shen, and M. Li. A comparative study use of OTL for many-objective optimization. The Genetic and Evolutionary Computation Conference (GECCO), Companion, 1411-1412, 2015.
R. Shen, J. Zheng, and M. Li. A hybrid development platform for evolutionary multi-objective optimization. IEEE Congress on Evolutionary Computation (CEC)1885-1892, 2015.
2014
M. Li, S. Yang, and X. Liu. Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Transactions on Evolutionary Computation, 18(3): 348-365, 2014. [PDF] [C code] [C code for WFG] [C++ code in OTL] [Read More]
M. Li, S. Yang, and X. Liu. Diversity comparison of Pareto front approximations in many-objective optimization. IEEE Transactions on Cybernetics, 44(12): 2568-2584, 2014. [PDF] [C code]
K. Li, Q. Zhang, S. Kwong, M. Li, R. Wang. Stable matching based selection in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 18(6): 909-923, 2014. [Java code] [Matlab code]
M. Li, S. Yang, K. Li, and X. Liu. Evolutionary algorithms with segment-based search for multiobjective optimization problems. IEEE Transactions on Cybernetics, 44(8): 1295-1313, 2014. [PDF] [C code]
M. Li, S. Yang, J. Zheng, and X. Liu. ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization. Evolutionary Computation, 22(2): 189-230, 2014. [PDF] [C code]
M. Li, S. Yang, and X. Liu. A test problem for visual investigation of high-dimensional multi-objective search. IEEE Congress on Evolutionary Computation (CEC), 2140-2147, 2014. (Best Student Paper Award). [PDF] [C code] [Read More]
2013
S. Yang, M. Li, X. Liu, and J. Zheng. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 17(5): 721-736, 2013. [PDF] [supplement] [C code] [C++ code in OTL] [Read More]
M. Li, S. Yang, X. Liu, and R. Shen. A comparative study on evolutionary algorithms for many-objective optimization. The 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO), 261-275, 2013. [PDF]
M. Li, S. Yang, X. Liu, and K. Wang. IPESA-II: Improved Pareto envelope-based selection algorithm II. The 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO), 143-155, 2013. [PDF] [C code]
2012 & earlier
K. Li, S. Kwong, J. Cao, M. Li, J. Zheng, and R. Shen. Achieving balance between proximity and diversity in multi-objective evolutionary algorithm. Information Sciences, 182(1): 220-242, 2012.
M. Li, J. Zheng, R. Shen, K. Li, and Q. Yuan. A grid-based fitness strategy for evolutionary many-objective optimization. The Genetic and Evolutionary Computation Conference (GECCO), 463-470, 2010. (Nominated to the Best Paper Award). [C code]
M. Li, J. Zheng, K. Li, Q. Yuan, and R. Shen. Enhancing diversity for average ranking method in evolutionary many-objective optimization. The 11th International Conference on Parallel Problem Solving from Nature (PPSN), 647-656, 2010. [C code]
M. Li and J. Zheng. Spread assessment for evolutionary multi-objective optimization. The 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO), 216-230, 2009.
M. Li, J. Zheng, K. Li, J. Wu, and G. Xiao. A spanning tree based method for pruning non-dominated solutions in multi-objective optimization problems. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4882-4887, 2009.
K. Li, J. Zheng, M. Li, C. Zhou, and H. Lv. A novel algorithm for non-dominated hypervolume-based multiobjective optimization. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 5220-5226, 2009.
M. Li, J. Zheng, and G. Xiao. An efficient multi-objective evolutionary algorithm based on minimum spanning tree. IEEE Congress on Evolutionary Computation (CEC), 617-624, 2008.
M. Li, J. Zheng, and G. Xiao. Uniformity assessment for evolutionary multi-objective optimization. IEEE Congress on Evolutionary Computation (CEC), 625-632, 2008.
M. Li, J. Zheng, and J. Wu. Improving NSGA-II algorithm based on minimum spanning tree. The 7th International Conference on Simulated Evolution And Learning (SEAL), 171-179, 2008.