Man-Fai is a Lecturer in Computing and Artificial Intelligence.
Man-Fai obtained his Bachelor of Science (Hons) and Master of Philosophy in Computing from Hong Kong Metropolitan University, Doctor of Philosophy in Computer Science from the City University of Hong Kong. Man-Fai worked at Hong Kong Metropolitan University as a Lecturer before joining ARU. His research interests include portfolio optimization, multiobjective optimization, and computational intelligence.
Computing and Artificial Intelligence
Guest Editor: Special Issue on Advances in Analysis and Application of Multi-Objective Memetic Optimization Algorithms, Memetic Computing (Impact factor 5.900)
Leung, M. F., Wang, J. & Li, D. (In press). Decentralized robust portfolio optimization based on cooperative-competitive multiagent systems. IEEE Transactions on Cybernetics. DOI:10.1109/TCYB.2021.3088884
Yuen, M.C., Ng, S.C., Leung, M.F. & Che, H. (In press). A Metaheuristic-Based Framework for Index-Tracking with Practical Constraints. Complex & Intelligent Systems. doi: 10.1007/s40747-021-00605-5
Leung, M. F. & Wang, J. (2022). A collaborative neurodynamic approach to cardinality-constrained bi-objective portfolio optimization. Neural Networks, 145, 68-79. DOI:10.1016/j.neunet.2021.10.007
Leung, M. F. & Wang, J. (2021). Minimax and bi-objective portfolio selection based on collaborative neurodynamic optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(7), 2825-2836. DOI:10.1109/TNNLS.2019.2957105
Dai, C., Che, H. & Leung, M. F. (2021). A neurodynamic optimization approach for L1 minimization with application to compressed image reconstruction. International Journal on Artificial Intelligence Tools, 30(1), 2140007. DOI:10.1142/S0218213021400078
Yuen, M.C., Ng, S.C. & Leung, M. F. (2021). A competitive mechanism multi-objective particle swarm optimization algorithm and its application to signalized traffic problem. Cybernetics and Systems, 52(1), 73-104. DOI:10.1080/01969722.2020.1827795
Leung, M. F. & Wang, J. (2018). A collaborative neurodynamic approach to multiobjective optimization. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5738-5748. DOI: 10.1109/TNNLS.2018.2806481
Lui, A. K. & Chan, Y. H. & Leung, M. F. (2021, December). Modelling of Destinations for Data-driven Pedestrian Trajectory Prediction in Public Buildings. 2021 IEEE International Conference on Big Data (IEEE BigData 2021) (Acceptance rate 19.7%) (pp 1-6). Orlando, FL, USA.
Leung, M. F. & Wang, J. (2021, December). Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection. In International Conference on Intelligent Control and Information Processing (ICICIP 2021) (pp 401-405). Dali, Yunnan, China.
Leung, M. F. & Ng, S.C. (2020, July) A hybrid algorithm based on MOEA/D and local search for multiobjective optimization. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp 1-8). Glasgow, United Kingdom.
Tam, H. H., Leung, M. F., Wang, Z., Ng, S. C., Cheung, C. C. & Lui, A. K. (2016, July). Improved adaptive global replacement scheme for MOEA/D-AGR. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 2153-2160).
Leung, M. F., Ng, S. C., Cheung, C. C. & Lui, A. K. (2015, May). A new algorithm based on PSO for multi-objective optimization. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 3156-3162).
Leung, M. F., Ng, S. C., Cheung, C. C. & Lui, A. K. (2014, July). A new strategy for finding good local guides in MOPSO. In 2014 IEEE Congress on Evolutionary Computation (CEC) (pp. 1990-1997).