杨圣祥导师主页
基本信息
姓名: 杨圣祥
职称:
单位电话:
电子信箱: syang@dmu.ac.uk
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个人简介

杨圣祥,教授,博士生导师。湖南省“芙蓉学者”讲座教授,英国 德蒙特福特大学教授。研究兴趣包括以下方面:遗传与进化计算,群体智能,分布估计算法,人工神经网络,动态优化问题,多目标优化,数据挖掘,智能系统。著有多部专著,发表SCI论文200余篇,被引5000余次,单片最高引用100余次。

个人主页:http://www.tech.dmu.ac.uk/~syang/



学习工作经历

    1993年、1996年和1999年分别在东北大学获得学士学位、硕士学位和博士学位。

199910 200010月在英国伦敦国王学院的一个算法设计团队做博士后研究助理。

200011月到20106月在莱斯特大学做讲师。

20107月到20126月在布鲁内尔大学信息系统和计算做高级讲师。

2012年至今任教于德蒙特福特大学计算机科学与信息学院。



科研项目

1. 动态多目标进化算法关键问题研究,国家自然科学基金面上项目,62万人民币,2017-2020年。

2. Evolutionary Computation for Dynamic Constrained Optimization Problems (ECDCOP)195455欧元,2016-2018年。

3. Evolutionary Computation for Dynamic Optimisation in Network Environments957292英镑,2013-2017年。

4. Evolutionary Computation for Optimised Rail Travel (EsCORT)62134英镑,2013-2015年。

5.  Evolutionary Algorithms for Dynamic Optimisation Problems: Design, Analysis and Applications571238英镑,2008-2011年。

6. Evolutionary Computation for Dynamic Scheduling Problems in Process Industries 30000英镑,2012-2013年。

7. Evolutionary Computation for Dynamic Optimization and Scheduling Problems15000英镑,2010-2011年。

8.  Improved Evolutionary Algorithms with Primal-Dual Population for Dynamic Variation in Production Systems10000英镑,2009-2010年。


代表性学术成果
  1. W. Gong, Y. Wang, Z. Cai, and S. Yang. A weighted biobjective transformation technique for      locating multiple optimal solutions of nonlinear equation systems. IEEE Transactions on Evolutionary      Computation, published online first: 15 March 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2670779 and Source Code in      C++).

  2. J. Eaton, S. Yang and      M. Gongora. Ant colony optimization for simulated dynamic multi-objective      railway junction rescheduling.IEEE      Transactions on Intelligent Transportation Systems, published online      first: 10 March 2017. IEEE Press (DOI: 10.1109/TITS.2017.2665042).

  3. Y. Wang, B. Xu, G. Sun, and S. Yang. A two-phase differential evolution for uniform      designs in constrained experimental domains.IEEE Transactions on Evolutionary Computation, published      online first: 17 February 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2669098 and Source Code in      Matlab).

  4. M. Li, C. Grosan, S.      Yang, X. Liu, and X. Yao. Multi-line distance minimization: A      visualized many-objective test problem suite.IEEE Transactions on Evolutionary Computation, published      online first: 18 January 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2655451 and Source Code in C).

  5. S. Jiang and S.      Yang. A strength pareto evolutionary algorithm based on reference      direction for multi-objective and many-objective optimization. IEEE Transactions on Evolutionary      Computation, accepted on 27 June 2016. IEEE Press (DOI:      10.1109/TEVC.2016.2592479 and Source Code in C).

  6. M. Mavrovouniotis, F. M. Muller and S. Yang. Ant colony optimization with local search for dynamic      travelling salesman problems.IEEE      Transactions on Cybernetics, published online first: 13 June 2016.      IEEE Press (DOI: 10.1109/TCYB.2016.2556742 andSource Code in      C++).

  7. S. Yang, S. Jiang and Y. Jiang. Improving the multiobjective      evolutionary algorithm based on decomposition with new penalty      schemes. Soft Computing,      published online first: 18 February 2016. Springer (DOI:      10.1007/s00500-016-2076-3).

  8. M. Mavrovouniotis, C. Li and S. Yang. A survey of swarm intelligence for dynamic      optimization: Algorithms and applications.Swarm and Evolutionary Computation, 33: 1-17, April 2017.      Elsevier (DOI:      10.1016/j.swevo.2016.12.005).

  9. R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao. A benchmark test suite for      evolutionary many-objective optimization. Complex and Intelligent Systems, 3(1): 67-81, March 2017.      Springer (DOI: 10.1007/s40747-017-0039-7 and Source Code in      Matlab).

  10. S. Jiang and S.      Yang. A steady-state and generational evolutionary algorithm for      dynamic multi-objective optimization. IEEE Transactions on Evolutionary Computation, 21(1): 65-82,      February 2017. IEEE Press (DOI: 10.1109/TEVC.2016.2574621 andSource Code in C).

  11. S. Jiang and S.      Yang. Evolutionary dynamic multi-objective optimization: benchmarks      and algorithm comparisons. IEEE      Transactions on Cybernetics, 47(1): 198-211, January 2017. IEEE Press      (DOI: 10.1109/TCYB.2015.2510698 and Source Code in C).

  12. Z. Li, J. Guo and S.      Yang. Improving the JADE algorithm by clustering successful      parameters. International      Journal of Wireless and Mobile Computing, 11(3): 190-197, December      2016. Inderscience Publishers Ltd (DOI: 10.1504/IJWMC.2016.081159).

  13. M. Li, S. Yang,      and X. Liu. Pareto or non-pareto: Bi-criterion evolution in      multi-objective optimization. IEEE      Transactions on Evolutionary Computation, 20(5): 645-665, October 2016.      IEEE Press (DOI: 10.1109/TEVC.2015.2504730).