About

I recieved the B.Sc. degree in information and computing science and the M.Sc. degree in control theory and control engineering from Northeastern University, Shenyang, China in 2011 and 2013, respectively. I received the Ph.D. degree in Computer Science from De Montfort University in 2017, financed by the Engineering and Physical Sciences Research Council (EPSRC). My principal PhD supervisor was Prof. Shengxiang Yang, and Prof. Ferrante Neri was my co-supervisor. I joined the Interdisciplinary Computing and Complex BioSystems (ICOS) group and started his postdoc research in 2017. I am now involved in the Portabolomics project, focusing on metabolic network modelling, flux balance analysis, and metabolic engineering.

Research Interests

I am mainly focusing on computational modelling, metabolic network optimisation, evolutionary computation, multiobjective and dynamic optimisation, etc.

News

  • 25-02-2018: Another nice research vist to Prof. Stelling's research group in ETH. Many thanks for hosting me with great help.
  • 12-02-2018: A nice research vist to Prof. Banga's research group in Vigo. Many thanks for hosting me with great help.
  • 19-01-2018: Source code of SDP-- A scalable test suite for dynamic multiobjective optimisation -- is now available here. Please feel free to download or disseminate it.
  • 10-01-2018: Our technical report for CEC2018 Competition on Dynamic Multiobjective Optimisation is now available here.
  • 28-12-2017: Our proposal for "Competition on Dynamic Multibjective Optimisation" has been accepted by "CEC2018"

Publications

(most of source codes can be available upon request)

2017

  • S. Jiang, 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. [link]
  • S. Jiang, S. Yang, Y. Wang, and X. Liu, “Scalarizing functions in decomposition- based evolutionary algorithms,” IEEE Transactions on Evolutionary Computation in press, 2017. [link]
  • 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. [link] [C++] [Matlab in PlatEMO]

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. [link] [Matlab in PlatEMO]
  • 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. [link]
  • S.Jiang and S.Yang,“Convergence versus diversity in multiobjective optimization,” in 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), 2016, in press.
  • S. Jiang and S. Yang, “Adaptive penalty scheme for multiobjective evolutionary algorithm based on decomposition,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC 2016), 2016.
  • S. Jiang, and S. Yang, “On the use of Hypervolume for diversity measurement of Pareto front approximations,” in Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence in Multi-Criteria Decision Making (CIMCDM), 2016.

2015

  • S. Yang, S. Jiang, and Y. Jiang, “Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes,” Soft Computing, vol. 21, no. 16, pp. 4677-4691, 2017.
  • S. Jiang and S. Yang, “A fast strength Pareto evolutionary algorithm incorporating predefined preference information,” in Proceedings of the 15th UK Workshop on Computational Intelligence (UKCI), 2015.
  • S. Jiang and S. Yang, “Approximating multiobjective optimization problems with complex Pareto fronts,” in 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,” in Proceedings of the 2014 IEEE Symposium Series on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2014, pp. 32-39
  • S. Jiang and S. Yang, “A benchmark generator for dynamic multi-objective optimization problems,” in Proceedings of the 14th UK Workshop on Computational Intelligence (UKCI), 2014, pp. 1-8.
  • S. Jiang and S. Yang, “An improved quantum-behaved particle swarm optimization based on linear interpolation,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), 2014, pp. 769-775.

PhD Theses

  • S. Jiang. Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization. Ph.D Thesis, De Montfort University, Leicester: UK, 2017.

Talks

2017

  • A Tutorial on Evolutionary Dynamic Multiobjective Optimisation, Shengxiang Yang and Shouyong Jiang, SSCI2017, USA, 3 Oct. 2017
  • Evolutionary Multiobjective Optimisation and Its Application to Metabolic Engineering, Shouyong Jiang, ICOS seminar, 3 Oct. 2017
  • Scalarizing Functions in Evolutionary Multiobjective Optimisation, Shouyong Jiang, EPSRC ECDONE Project, University of Birmingham, 25 Jul. 2017

2016

  • Convergence versus diversity in multiobjective optimization, Shouyong Jiang, 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), Edinburgh, U.K., Sep. 2016.
  • Dynamic multiojective optimization: Algorithmic design and challenges, Shouyong Jiang, Research Workshop on Computational Intelligence, De Montfort University, Leicester, U.K., May 2016.

2015

  • Approximating multiobjective optimization problems with complex Pareto fronts, Shouyong Jiang 15th UK Workshop on Computational Intelligence (UKCI), Exeter, U.K., September, 2015.
  • A fast strength Pareto evolutionary algorithm incorporating predefined preference information, Shouyong Jiang, 15th UK Workshop on Computational Intelligence (UKCI), Exeter, U.K., September, 2015.

2014

  • A framework of scalable dynamic test problems for dynamic multi-objective optimization, Shouyong Jiang, 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Orlando, USA, December, 2014.
  • Benchmarking dynamic multiobjective test problems, Shouyong Jiang, Doctor Training Programme, De Montfort University, Leicester, U.K., July 2014.
Last update:. Any opinions expressed on this page are those of the author.