Xiaodong Li received his Ph.D. degree in artificial intelligence from University of Otago, Dunedin, New Zealand. His research interests include machine learning, evolutionary computation, multiobjective optimization, multimodal optimization (niching), swarm intelligence, data mining/analytics, deep learning, journey planning, math-heuristic methods for optimization. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award". He is an IEEE Fellow. He is also a member of ARC (Australian Research Council) College of Experts (2023 - 2025).
- PhD in Artificial Intelligence, 1998
- Dip.Com in Information Science, 1992
- B.Sc. in Information Science, 1988
- Senior Member, Institute of Electrical and Electronics Engineers (IEEE).
- Member, IEEE Computational Intelligence Society.Vice Chair, Computational Intelligence Chapter, IEEE Victorian Section, Melbourne, Australia
- Member, Steering committee of Simulated Evolution And Learning (SEAL).
- Ma, X.,Huang, Z.,Li, X.,Qi, Y.,Wang, L.,Zhu, Z. (2023). Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A Survey In: IEEE Transactions on Cybernetics, 53, 3702 - 3715
- Shen, Y.,Sun, Y.,Li, X.,Eberhard, A.,Ernst, A. (2023). Adaptive solution prediction for combinatorial optimization In: European Journal of Operational Research, 309, 1392 - 1408
- Weiner, J.,Ernst, A.,Li, X.,Sun, Y. (2023). Ranking constraint relaxations for mixed integer programs using a machine learning approach In: EURO Journal on Computational Optimization, 11, 1 - 29
- Li, X.,Lv, Y.,Wen, G.,Yu, X. (2023). Tracking Consensus of Multi-agent Systems with Varying Number of Agents under Actuator Attacks In: IEEE Transactions on Circuits and Systems II: Express Briefs, , 1 - 5
- Chu, R.,Chik, L.,Chan, J.,Gutzmann, K.,Li, X. (2023). Automatic meter error detection with a data-driven approach In: Engineering Applications of Artificial Intelligence, 123, 1 - 13
- Shen, Y.,Sun, Y.,Li, X.,Eberhard, A.,Ernst, A. (2023). 3. Enhancing column generation by a machine-learning-based pricing heuristic for graph coloring In: Proceedings of the AAAI Conference on Artificial Intelligence, online, 28 February - 1 March 2022
- Sun, Y.,Esler, S.,Thiruvady, D.,Ernst, A.,Li, X.,Morgan, K. (2022). Instance space analysis for the car sequencing problem In: Annals of Operations Research, , 1 - 23
- Islam, M.,Li, X.,Deb, K. (2022). A speciation-based bilevel niching method for multimodal truss design problems In: Journal of Combinatorial Optimization, 44, 172 - 206
- Omidvar, M.,Li, X.,Yao, X. (2022). A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization - Part II In: IEEE Transactions on Evolutionary Computation, 26, 823 - 843
- Blair, A.,Khodadadian Gostar, A.,Tennakoon, R.,Bab-Hadiashar, A.,Li, X.,Palmer, J.,Hoseinnezhad, R. (2022). Distributed Multi-Sensor Control for Multi-Target Tracking In: Proceedings of the 11th International Conference on Control, Automation and Information Sciences, Hanoi, Vietnam, 21/11/2022-24/11/2022
- Machine learning techniques for fuel loss detection at service stations. Funded by: ARC Linkage Project Grants 2019 from (2021 to 2024)
- Research Challenge Topic – 23: Deep reasoning reinforcement learning for cognitive information warfare - AI for Decision Making (Administered by University of Melbourne). Funded by: Defence Science and Technology Group (scheme) - competitive from (2020 to 2021)
- A Novel and Efficient Approach for Optimization involving Iterative Solvers (administered by UNSW). Funded by: ARC Discovery Project via Other University from (2019 to 2022)
- Hybrid methods with decomposition for large scale optimization. Funded by: ARC Discovery Projects 2018 from (2018 to 2021)
- Optimisation and machine learning for wetstock management. Funded by: DIIS - Innovations Connections - Competitive from (2018 to 2019)
Note: Supervision projects since 2004
22 PhD Completions and 1 Masters by Research Completions7 PhD Current Supervisions
Artificial intelligence; learning algorithms; neural networks; connectionist learning models; evolutionary computation; genetic algorithms; parallel GA; genetic programming; artificial life; complex systems; adaptive systems; emergent behaviours; cellular automata; multi-agent simulation; intelligent agents; swarm intelligence; ant colony algorithms; percolation; self-organized criticality; phase transition.