Shouyong Jiang
Computer Scientist, Data Researcher.
Contact
|
AI and Data Research Group Department of Computer Science University of Aberdeen Aberdeen AB24 3FX United Kingdom |
https://scholar.google.co.ukhttps://chang88ye.github.io/homepageshouyong.jiang@abdn.ac.ukhttps://www.researchgate.net/profile/Shouyong-Jianghttps://orcid.org/0000-0001-5099-2093 |
Research Interests
Artificial Intelligence, Data Science, Optimisation and Decision Making, Evolutionary Learning.
Education
2017
PhD in Computer Science, De Montfort University, Leicester, UK (11/2013 - 3/2017). Dissertation: “Evolutionary Algorithms for Multi-objective Optimisation in Static and Dynamic Environments”.
2013
MSc in Control Systems, Northeastern University, Shenyang, China (9/2011 - 7/2013).
2011
BSc in Mathematics, Northeastern University, Shenyang, China (9/2007 - 7/2011).
Academic Career
2021
Lecturer in Artificial Intelligence, University of Aberdeen, Aberdeen, UK.
2019
Lecturer in Machine Learning, University of Lincoln, Lincoln, UK.
2018
Visiting Scholar, Bio Process Engineering Group, IIM-CSIC, Vigo, Spain.
Visiting Scholar, Computational Systems Biology Group, ETH, Zurich, Swiss.
2017
Research Associate, ICOS Group, Newcastle University, Newcastle upon Tyne, UK.
2013
Ph.D Researcher, Centre for Computational Intelligence, De Montfort University, Leicester, UK.
Honors & Awards
2021
Outstanding PhD Dissertation Award, Computational Intelligence Society (CIS), IEEE
2017
Doctoral Thesis Prize, De Montfort University, UK
2016
Outstanding Students Abroad, China Scholarship Council (CIS)
Travel Bursary, PPSN Conference, Edinburg, UK
2015
Travel Award, UKCI Conference, Exeter, UK
2014
Conference & Travel Award, De Montfort University, UK
2013
PhD Research Scholarship, EPSRC + De Montfort University, UK
Publications
Journals
2022
Y. Hu, J. Zheng, S. Jiang, et al., “Handling Dynamic Multi-objective Optimization Environments via Layered Prediction and Subspace-based Diversity Maintenance”, IEEE Transactions on Cybernetics, 2022, accepted.
J. Liu, Y. Wang, P.Q. Huang and S. Jiang, “CaR: A cutting and repulsion-Based evolutionary framework for mixed-integer programming problems,” IEEE Transactions on Cybernetics, 2022, accepted.
2021
L. Gong, M. Yu, S. Jiang, V. Cutsuridis, S. Kollias, S. Pearson, “Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN,” Sensors, 21(13), 4537.
Q Y Zhang, X Y He, Y Q Dong, S Y Jiang and H Song, “Optimizing the Flight Route of UAV Using Biology Migration Algorithm”, Journal of Physics: Conference Series 2025 012052, 2021.
L. Gong, M. Yu, S. Jiang, V. Cutsuridis, S. Kollias, S. Pearson, “Studies of Evolutionary Algorithms for The Reduced Tomgro Model Calibration for Modelling”, Smart Agricultural Technology 1, 100011.
Q. Zhang, S. Jiang, S. Yang, H. Song, “Solving Dynamic Multi-objective Problems with A New Prediction-based Optimization Algorithm”, PLOS One, 16(8), e0254839.
Y. Hu, J. Zheng, S. Jiang, et al., “Dynamic Multi-Objective Optimization Algorithm Based Decomposition”, Information Sciences, 2021, accepted.
B．Alhnaity, S. Kollias, G. Leontidis, S. Jiang, B. Schamp, S. Pearson, “An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth,” Information Sciences, 560: 35–50, 2021.
2020
V. Cutsuridis, S. Jiang, M. J. Dunn, A. Rosser, J. Brawn, J.T. Erichsen, “Neural modelling of antisaccade performance of healthy controls and early huntington’s disease patients,” Chaos, 31(1), 013121, 2020.
S. Jiang, Y. Wang, M. Kaiser, N. Krasnogor, “NIHBA: A network interdiction approach for metabolic engineering design”, Bioinformatics, vol. 36, no. 11, 3482–3492, 2020.
J. Guo, Y. Wu, W. Xie, S. Jiang, “Triangular Gaussian mutation to differential evolution”, Soft Computing, 24(12), 9307 - 9320, 2019.
2019
Q. Zhang, S. Yang, S. Jiang, R. Wang, X. Li, “Novel prediction strategies for dynamic multi-objective optimization”, IEEE Transactions on Evolutionary Computation, vol. 24, no. 2, 260-274, 2019.
Y. Wang, J. Yu, S. Yang, S. Jiang, and S. Zhao, “Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons”, Swarm and Evolutionary Computation, vol. 50, 2019, 100559.
S. Jiang, M. Kaiser, S. Yang, S. Kollias, and N. Krasnogor, “A scalable test suite for continuous dynamic multiobjective optimisation”, IEEE Transactions on Cybernetics, vol. 50, no. 6, 2814 – 2826, 2019
2018
S. Jiang, S. Yang, Y. Wang, and X. Liu, “Scalarizing functions in decomposition-based evolutionary algorithms”, IEEE Transactions on Evolutionary Computation, vol. 22, no. 2, pp. 296—313, 2018. (IF: 10.629)
H. Li, J. Hu, and S. Jiang, “A hybrid PSO based on dynamic clustering for global optimization”, IFAC-PapersOnline, vol. 51, no. 8, pp. 269—274, 2018.
S. Yang, S. Jiang, and Y. Jiang, “Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes,” Soft Computing, vol. 17, no. 16, pp. 4677—4691, 2017. (IF: 2.472)
2017
S. Jiang and S. Yang, “A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, no. 1, pp. 65—82, 2017 (IF: 10.629)
S. Jiang and S. Yang, “A strength Pareto evolutionary algorithm based on reference direction for multiobective and many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, no. 3, pp. 329—346, 2017 (IF: 10.629)
S. Jiang and S. Yang, “Evolutionary dynamic multi-objective optimization: benchmarks and algorithm comparisons,” IEEE Transactions on Cybernetics, vol. 47, no.1, pp. 198—211, 2017. (IF: 7.384)
2016
S. Jiang and S. Yang, “An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts,” IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 421—437, 2016. (IF: 7.384)
2014
W. Qian, H. Hou and S. Jiang, “New self-adaptive cuckoo search algorithm,” Computer Science (China), vol. 41, no. 7, pp.279-282, Jun. 2014.
Conferences
2021
S. Jiang, J. Guo, B. Alhnaity, and Q. Zhang, “On analysis of irregular Pareto front shapes,” EMO2021.
J. Guo, M. Shao, S. Jiang, and X. Zhou, “A niche based multi-objective particle swarm optimizer”, CEC 2021, 1319 – 1326.
2020
J. Guo, S. Jiang, et al., “An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme”, GECCO 2020.
2018
M. Torres, S. Jiang, D. Pelta, M. Kaiser and N. Krasnogor, “Strain design as multiobjective network interdiction problem: A preliminary approach”, LNAI, CAEPIA 2018.
S. Jiang, M. Torres, D. Pelta, P. Krabben, M.Kaiser and N. Krasnogor, “Improving microbial strain design via multiobjective optimisation and decision making,’’ AI for Synthetic Biology, IJCAI / FAIM 2018, 2018.
S. Jiang, M. Kaiser, S. J. Guo, S. Yang, and N. Krasnogor, “Less detectable environmental changes in dynamic multiobjective optimisation”, GECCO’18, 2018.
S. Jiang, M. Kaiser, S. Wan, J. Guo, S. Yang, and N. Krasnogor, “An empirical study of dynamic triobjective optimisation problems”, CEC’18, 2018.
2016
S. Jiang and S. Yang, “Convergence versus diversity in multiobjective optimization”, The 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), 2016.
S. Jiang and S. Yang, “On the use of hypervolume for diversity measurement of Pareto front approximations”, SSCI’16, 2016.
S. Jiang and S. Yang, “Adaptive penalty scheme for multiobjective evolutionary algorithm based on decomposition”, Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC 2016), 2016.
2015
S. Jiang and S. Yang, “A fast strength Pareto evolutionary algorithm incorporating predefined preference information,” Proceedings of the 15th UK Workshop on Computational Intelligence (UKCI), 2015.
S. Jiang and S. Yang, “Approximating multiobjective optimization problems with complex Pareto fronts,” Proceedings of the 15th UK Workshop on Computational Intelligence (UKCI), 2015.
2014
S. Jiang and S. Yang, “A framework of scalable dynamic test problems for dynamic multi-objective optimization,” Proceedings of the 2014 IEEE Symposium Series on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 32-39, 2014
S. Jiang and S. Yang, “A benchmark generator for dynamic multi-objective optimization problems,” Proceedings of the 14th UK Workshop on Computational Intelligence (UKCI), pp. 1-8, 2014.
S. Jiang and S. Yang, “An improved quantum-behaved particle swarm optimization based on linear interpolation,” Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 769-775, 2014.
Reports
2018
S. Jiang, S. Yang, X. Yao, K. C. Tan, M. Kaiser, and N. Krasnogor, “Benchmarks for CEC18 competition on dynamic multiobjective optimization,’’ CEC 2018, Rio, Brazil.
Invited Talks
“Metabolic games in metabolic engineering design”, invited talk, Newcastle University, UK, Jan., 2020.
“A game-theoretic approach to strain design for metabolic engineering”, invited talk, University of Cambridge, UK, July 2019.
“Computational strain design: a network interdiction approach”, ICOS seminar, Newcastle University, UK, 2019.
“Less detectable environmental changes in dynamic multiobjective optimisatiom”, GECCO2018, Kyoto, Japan, 2018.
“An empirical study of dynamic triobjective optimisation problems”, CEC2018, Rio, Brazil, 2018.
“Evolutionary multiobjective optimisation in computational metabolic engineering’’, Bioprocess Engineering Group, University of Vigo, Spain, 2018. “Evolutionary computing in real-world applications”, ICOS seminar, Newcastle University, UK, 2017.
“Evolutionary computation: theory and application in synthetic biology”, EPSRC workshop, University of Birmingham, UK, 2017.
“Computational modelling for metabolic engineering” 4th International Synthetic & Systems Biology Summer School, University of Cambridge, UK.
“Convergence versus diversity in multiobjective optimization”, 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), Edinburgh, U.K., September 2016.
“Dynamic multiojective optimization: Algorithmic design and challenges”, Research Workshop on Computational Intelligence, De Montfort University, Leicester, U.K., May 2016.
“Approximating multiobjective optimization problems with complex Pareto fronts,” 15th UK Workshop on Computational Intelligence (UKCI), Exeter, U.K., September, 2015.
“A fast strength Pareto evolutionary algorithm incorporating predefined preference information,” 15th UK Workshop on Computational Intelligence (UKCI), Exeter, U.K., September, 2015.
“A framework of scalable dynamic test problems for dynamic multi-objective optimization,” 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Orlando, USA, December, 2014.
“Benchmarking dynamic multiobjective test problems”, Doctor Training Programme, De Montfort University, Leicester, U.K., July 2014.
Teaching
CS1527 Object Oriented Programming (Module Coordinator, Aberdeen)
CS551P Advanced Programming (Instructor, Aberdeen)
CS3033 Artificial Intelligence (Instructor, Aberdeen)
CMP3751 Machine Learning (Module Coordinator, Lincoln)
CMP3972 Big Data (Instructor, Lincoln)
CMP1902 Programming Fundamentals (Instructor, Lincoln)
Funding
2022
Food supply chain emissions modelling, £20K, Scottish Food and Drink Net Zero Challenge, PI
Computational modelling of B. subtlis metabolism, £ 11K, BBSRC Mitigation Fund, PI
2019
SmartGreen: Big data and eco‑innovative resource use in the North Sea Region Greenhouse industry, £3,200K, EU-Interreg, Co-I
2017
Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies, £7,000K, EPSRC, Research Associate
2013
ECDONE: Evolutionary computation for dynamic optimisation in network environments, £957K, EPSRC, Researcher
Supervision
PhD students
Matthew Beddows, University of Aberdeen (2021). PhD project: Deep learning for soft fruit yield prediction.
Zhuoran Li, University of Lincoln (2021). PhD project: Machine learning for financial market analysis.
MSc students
Prasanth Sivaprasad, University of Aberdeen (2021). Thesis: Eco-advisor: The energy optimisation tool.
Ting Wang, University of Aberdeen (2021). Thesis: Reinforcement learning for solving vehicle routing and loading problems.
Petra Ekhator, University of Aberdeen (2021). Thesis: Investigation of AI approaches for crime prediction.
Ryan Mottram, University of Lincoln (2020). Thesis: Machine learning for detecting abnormal cyber activities.
Tom A S Hindle, University of Lincoln (2020). Thesis: AI driven smart policing.
Sajid Rafique, University of Lincoln (2020). Thesis: Machine learning for tomato yeild prediction.
Administration
Go Abroad Tutor (Aberdeen)
Programme Lead of MSc Data Science and Applications (Founder, Lincoln)
Professional Activities
Membership
2021 Memmber, UKRI Future Leaders Fellowships Peer Review College
2021 Fellow, Higher Education Academy
2020 Member, ESPRC Peer Review College
2020 Member, NERC Peer Review Associate College
2020 Member, IEEE Technical Committee in Soft Computing
Editoral Board & Reviewer
2022 Member of Editorial Board, Journal of Sensors
2022 Guest Editor, Special Issue on “Cloud Computing and IoT for Intelligent Applications”, Wireless Communications and Mobile Computing
2021 Guest Editor, Special Issue on “Machine Learning in Bioinformatics”, MDPI Algorithms
2020 Member of Editorial Board, PLOS ONE
2020 Reviewer, National Institute for Health Research (NIHR)
2020 Judge Panellist, Global Undergraduate Awards
2015 Peer Reviewer, IEEE TEVC, IEEE TCYB, Knowledge‑based Systems, Ocean Engineering, etc
Conference PC Member
2022 Member, World Congress on Computational Intelligence (WCCI, including IJCNN and CEC), IEEE
2021 Member, International Joint Conference on Neural Networks, IEEE
2020 Member, International Conference on Evolutionary Multi‑criteria Optimisation, Springer
2018 Member, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments, IEEE CIS
2018 Organiser, CEC competition on Dynamic Multi‑objective Optimisation, WCCI