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I am currently an Associate Professor in the School of Management at Tianjin University of Commerce, China. I received my B.E. degree in Logistics Engineering from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2011. I received the M.Sc. in Industrial Engineering and the Ph.D. degree in Management Science and Engineering (under the supervision of Prof. Zhen He) from Tianjin University, Tianjin, China, in 2016. I was a Visiting Scholar (under the supervision of Prof. Mengjie Zhang and Prof. Bing Xue) at the Evolutionary Computation Research Group (ECRG), Victoria University of Wellington (VUW), Wellington, New Zealand, in 2018.
I focus on the research in Quality Engineering and Management using the methods in the Machine Learning and Computational Intelligence contexts (such as Feature Selection and Evolutionary Algorithms). My research interests include:
Key Quality Feature (or Process Variable) Identification
Quality Prediction and Control
Evolutionary Multi-objective Optimization (EMO)
Evolutionary Computation (EC)
Feature Selection (with EC techniques)
Robust Multi-response Optimization
* denotes the corresponding author
Journal Papers
Li, A.-D., He, Z., Wang, Q., Zhang, Y.*, & Ma, Y.* (2025). A multi-objective evolutionary algorithm with mutual-information-guided improvement phase for feature selection in complex manufacturing processes. European Journal of Operational Research, 323(3), 952-965.
https://doi.org/10.1016/j.ejor.2024.12.036 BibTeX
pdf supplementary material Source Code
Li, A.-D., Zhang, Y.*, Zhang, M., & Meng, F. (2025). Quality improvement of magnetron in Company T based on Six Sigma. International Journal of Lean Six Sigma, 16(1), 89-108.
https://doi.org/10.1108/IJLSS-03-2022-0062 BibTeX
pdf
Li, A.-D.*, Xue, B., & Zhang, M. (2023). Multi-objective particle swarm optimization for key quality feature selection
in complex manufacturing processes. Information Sciences, 641, 119062.
https://doi.org/10.1016/j.ins.2023.119062 BibTeX
pdf Source Code
Liu, X., & Li, A.-D.* (2023). An improved probability-based discrete particle swarm optimization
algorithm for solving the product portfolio planning problem. Soft Computing.
doi:10.1007/s00500-023-08530-0 BibTeX
Source Code
Li, A.-D., He, Z., & Zhang, Y.* (2022). Robust multi-response optimization considering location effect, dispersion effect,
and model uncertainty using hybridization of NSGA-II and direct multi-search. Computers & Industrial Engineering, 169, 108247.
doi:10.1016/j.cie.2022.108247 BibTeX pdf
Source Code
He, Z., Hu, H., Zhang, M., Zhang, Y., & Li, A.-D.* (2022). A decomposition-based multi-objective particle swarm optimization
algorithm with a local search strategy for key quality characteristic identification in production processes.
Computers & Industrial Engineering, 172, 108617.
doi:10.1016/j.cie.2022.108617 BibTeX pdf
Li, A.-D.*, Xue, B., & Zhang, M. (2021). Improved binary particle swarm optimization for feature selection
with new initialization and search space reduction strategies. Applied Soft Computing, 106, 107302.
doi:10.1016/j.asoc.2021.107302 BibTeX pdf
Source Code
Li, A.-D.*, & He, Z. (2020). Multiobjective feature selection for key quality characteristic identification
in production processes using a nondominated-sorting-based whale optimization algorithm. Computers & Industrial Engineering,
149, 106852. doi:10.1016/j.cie.2020.106852
BibTeX pdf Source Code
Li, A.-D.*, Xue, B., & Zhang, M. (2020). Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Information Sciences, 523, 245–265. doi:10.1016/j.ins.2020.03.032 BibTeX pdf
Li, A.-D.*, He, Z., Wang, Q., & Zhang, Y.* (2019). Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method. European Journal of Operational Research, 274(3), 978–989. doi:10.1016/j.ejor.2018.10.051 BibTeX pdf
Li, A.-D., He, Z.*, & Zhang, Y. (2016). Bi-objective variable selection for key quality characteristics selection based on a modified NSGA-II and the ideal point method. Computers in Industry, 82, 95–103. doi:10.1016/j.compind.2016.05.008 BibTeX
Zhang, Y., Shang, Y., Hu, X., & Li, A.-D.* (2022). An improved exponential EWMA chart for monitoring time between events. Quality and Reliability Engineering International. doi:10.1002/qre.3102 BibTeX
Zhang, Y., Shang, Y., & Li, A.-D.* (2021). Self-information-based weighted CUSUM charts for monitoring Poisson count data with varying sample sizes. Quality and Reliability Engineering International, 37(5), 1847–1862. doi:10.1002/qre.3102 BibTeX
Conference Papers
Li, A.-D., Xue, B., Zhang, M., Lin, X., & Wang, G. (2025). Cooperative Coevolutionary Probability-Based Binary Particle Swarm Optimization for High-Dimensional Feature Selection. In 2025 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-4).
https://doi.org/10.1109/CEC65147.2025.11043034 BibTeX
Li, A.-D.*, Xue, B., & Zhang, M. (2021). A Forward Search Inspired Particle Swarm Optimization Algorithm for Feature Selection in Classification.
IEEE Congress on Evolutionary Computation, CEC 2021, Kraków, Poland, June 28 - July 1, 2021, 786–793.
doi:10.1109/CEC45853.2021.9504949 BibTeX
Source Code pdf
Li, A.*, & He, Z. (2016). ReliefF Based Forward Selection Algorithm to Identify CTQs for Complex Products. Proceedings of the 22nd
International Conference on Industrial Engineering and Engineering Management 2015. Paris: Atlantis Press.
doi:10.2991/978-94-6239-180-2_3 BibTeX
Liu, X.*, Xia, Y., Chen, M., & Li, A.-D. (2019). Integrating Assembly Line Balancing in Product Family Planning Design under the Multinomial Logit Choice Model. 2019 International Conference on Industrial Engineering and Systems Management (IESM), 1–6. doi:10.1109/IESM45758.2019.8948102 BibTeX
Journal Papers in Chinese
李岸达,何桢 & 何曙光.(2016).基于NSGA-Ⅱ的非平衡制造数据关键质量特性识别. 系统工程理论与实践,36(06),1472-1479. pdf
李岸达,何桢 & 何曙光.(2015).基于GSA的复杂产品关键质量特性识别. 系统工程与电子技术,37(09),2073-2079. pdf
李岸达,何桢 & 王庆.(2019).基于多目标鲸鱼优化的关键质量特性识别方法. 系统工程,37(01),134-142. pdf
李岸达,何桢 & 何曙光.(2014).基于Filter与Wrapper的复杂产品关键质量特性识别. 工业工程与管理,19(03),53-59. doi:10.19495/j.cnki.1007-5429.2014.03.009 pdf
闫伟,何桢 & 李岸达.(2014).基于CEM-IG算法的复杂产品关键质量特性识别. 系统工程理论与实践,34(05),1230-1236. pdf
2022-2024: Key Quality Factor Identification and Online Quality Prediction for Complex Manufacturing Processes Based on Machine Learning Approaches. National Natural Science Foundation of China (NSFC). Grant: 300,000 CNY. (PI)
2019-2021: Key Quality Characteristic Identification for Multi-stage Manufacturing Processes of Complex Products. Humanities and Social Sciences Youth Fund of Ministry of Education of China. Grant: 80,000 CNY. (PI)
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