Professor Xiaodong Li
College / Portfolio: STEM College
School / Department: STEM|School of Computing Technologies
Campus: City Campus
Contact me about: Research supervision
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).
- Thiruvady, D.,Nguyen, S.,Shiri, F.,Zaidi, N.,Li, X. (2022). Surrogate-assisted population based ACO for resource constrained job scheduling with uncertainty In: Swarm and Evolutionary Computation, 69, 1 - 16
- Sun, Y.,Wang, S.,Shen, Y.,Li, X.,Ernst, A.,Kirley, M. (2022). Boosting ant colony optimization via solution prediction and machine learning In: Computers and Operations Research, 143, 1 - 16
- 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
- Li, X.,Ma, X.,Zheng, Y.,Zhu, Z.,Wang, L.,Qi, Y.,Yang, J. (2021). Improving Evolutionary Multitasking Optimization by Leveraging Inter-Task Gene Similarity and Mirror Transformation In: IEEE Computational Intelligence Magazine, 16, 38 - 53
- Liu, D.,Qi, Y.,Yang, R.,Quan, Y.,Li, X.,Miao, Q. (2021). A tri-objective preference-based uniform weight design method using Delaunay triangulation In: Soft Computing, 25, 9703 - 9729
- Taylor, K.,Ha, H.,Li, M.,Chan, J.,Li, X. (2021). Bayesian Preference Learning for Interactive Multi-objective Optimisation In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France, 10 -14 July 2021
- Ma, X.,Huang, Z.,Li, X.,Wang, L.,Qi, Y.,Zhu, Z. (2021). Merged Differential Grouping for Large-scale Global Optimization In: IEEE Transactions on Evolutionary Computation, 99, 1 - 13
- Ma, X.,Huang, Z.,Li, X.,Qi, Y.,Wang, L.,Zhu, Z. (2021). Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A Survey In: IEEE Transactions on Cybernetics, , 1 - 14
- 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
21 PhD Completions7 PhD Current Supervisions and 1 Masters by Research 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.