Biography
Yahui Jia (Male, born 1992), Ph.D., is an Associate Professor and Doctoral Supervisor at the School of Future Technology, South China University of Technology, specializing in Artificial Intelligence (AI). He is primarily engaged in research on Artificial Intelligence and Computational Intelligence, focusing on Evolutionary Computation optimization methods and Evolutionary Computation learning methods. His key application areas include traditional combinatorial optimization problems, smart transportation, and smart logistics. He serves as a reviewer for top international journals, including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics, and has been repeatedly invited to serve as a Program Committee member for major international conferences in the field, such as IEEE WCCI.
Contact: jiayahui@scut.edu.cn
Personal Website
Education
2013-2019, Ph.D., Sun Yat-sen University, China
2009-2013, B.S., Sun Yat-sen University, China
Work Experience
2021-Present, Associate Professor (Pre-tenure), South China University of Technology
2019-2021, Postdoctoral Researcher (Supervision of Prof. Mengjie Zhang), Victoria University of Wellington, New Zealand
Selected Publications
[1] Ya-Hui Jia, Yi Mei, Mengjie Zhang, “A Two Stage Swarm Optimizer for Water Distribution Network Optimization,” IEEE Transactions on Cybernetics.
[2] Ya-Hui Jia, Yi Mei, Mengjie Zhang, “A Bi-level Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem”, IEEE Transactions on Cybernetics, 2021.
[3] Ya-Hui Jia, Yi Mei, Mengjie Zhang, “Contribution-based Cooperative Co-evolution for Non-separable Large-scale Problems with Overlapping Subcomponents,” IEEE Transactions on Cybernetics, 2020.
[4] Ya-Hui Jia, Wei-Neng Chen, Tianlong Gu, et al., “Distributed Cooperative Co-evolution with Adaptive Computing Resource Allocation for Large Scale Optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 2, pp. 188-202, 2018.
[5] Ya-Hui Jia, Wei-Neng Chen, Tianlong Gu, et al., “A Dynamic Logistic Dispatching System With Set-Based Particle Swarm Optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 9, pp. 1607-1621, 2018.
[6] Ya-Hui Jia, Wei-Neng Chen, Huaqiang Yuan, et al., “An Intelligent Cloud Workflow Scheduling System with Time Estimation and Adaptive Ant Colony Optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 634-649, 2021.
[7] Wei-Neng Chen, Ya-Hui Jia, Feng Zhao, et al., “A Cooperative Co-evolutionary Approach to Large-Scale Multisource Water Distribution Network Optimization,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 842-857, 2019.
[8] Guanqiang Gao, Yi Mei, Ya-Hui Jia, et al., “Adaptive Coordination Ant Colony Optimization for Multi-Point Dynamic Aggregation,” IEEE Transactions on Cybernetics, 2020.
[9] Guanqiang Gao, Yi Mei, Bin Xin, Ya-Hui Jia, Will N. Browne, “Automated Coordination Strategy Design using Genetic Programming for Dynamic Multi-Point Dynamic Aggregation,” IEEE Transactions on Cybernetics, 2021.
[10] Ya-Hui Jia, Yu-Ren Zhou, Ying Lin, et al., “A Distributed Cooperative Co-evolutionary CMA Evolution Strategy for Global Optimization of Large-Scale Overlapping Problems,” IEEE Access, vol. 7, pp. 19821-19834, 2019.
[11] Ya-Hui Jia, Yi Mei, Mengjie Zhang, “A Memetic Level-based Learning Swarm Optimizer for Large-scale Water Distribution Network Optimization”, in Proceedings of the 2020 Annual Conference on Genetic and Evolutionary Computation, pp. 1107-1115.
[12] Ya-Hui Jia, et al., “Generating Software Test Data by Particle Swarm Optimization,” in Proceedings of Asia-Pacific Conference on Simulated Evolution and Learning 2014, pp. 37-47.
[13] Ya-Hui Jia, Wei-Neng Chen, and Xiao-Min Hu, “A PSO approach for software project planning,” in Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 7-8.
[14] Guanqiang Gao, Yi Mei, Bin Xin, Ya-Hui Jia, Will N. Browne, “A Memetic Algorithm for the Task Allocation Problem on Multi-robot Multi-point Dynamic Aggregation Missions,” in Proceedings of the 2020 IEEE Congress on Evolutionary Computation, pp. 1-8.
