郑州大学计算智能实验室

Computational Intelligence Laboratory

梁静(负责人)



undefined

 博士,教授,博士生导师

 郑州大学电气工程学院院长

E-mail:liangjing@zzu.edu.cn





梁静,教授,博士生导师,2009年于新加坡南洋理工大学获得博士学位。现任郑州大学电气工程学院院长,国家优秀青年科学基金获得者,河南省教育厅学术技术带头人,中原青年拔尖人才,河南省青年科技工作者协会会长,河南省优化与智能控制技术工程研究中心负责人,河南省“现代工业过程智能优化与控制”创新型科研团队负责人,郑州大学计算智能实验室负责人。教育部高等学校科学研究优秀成果奖、河南省科学进步奖、河南省教育厅科技成果奖、2014 IEEE CIS Outstanding PhD Dissertation Award等奖获得者。IEEE Computational Intelligence Magazine, Swarm and Evolutionary Computation的Associate Editor,IEEE Transaction On Evolutionary Computation, IEEE Transactions on Neural Networks等多个JCR一区、二区的国际期刊评审专家。新加坡南洋理工大学河南校友会秘书长、河南省青年科技工作者协会生命科学类别副秘书长、郑州大学博士后联谊会流动站副会长,河南省侨联青年委员会副会长,河南欧美同学会理事。主持完成国家自然科学基金青年基金项目一项、中国博士后特别资助项目一项、中国博士后面上项目一项、国家自然科学基金面上项目一项,现主持在研国家优秀青年科学基金一项、国家自然科学基金面上项目一项、河南省教育厅创新人才项目一项、并以核心参与人员参与国家自然科学基金面上项目一项、教育厅科技研究重点项目一项、教育部博士点基金项目两项。研究方向为进化计算,多模态优化,多目标优化,神经网络,粒子群优化算法,差分进化等。发表论文共计120余篇,其中SCI/EI论文90余篇,JCR 1区论文38篇,截止目前,Google Citation被引用总次数为14000余次,h指数为41。发表的《Comprehensive learning particle swarm optimizer for global optimization of multimodal functions》一文荣登进化计算(Evolutionary Computation)领域十年高被引文章首位,Google Scholar单篇引用频次为3200余次,据web of science数据库统计,来自包括美国、澳大利亚、加拿大、新加坡等14个国家和地区的不同学者直接在该篇文章的基础上进行了算法创新型理论性研究或直接应用型研究,累计延伸出将近120余篇论文。现发表国家发明专利2项,授权软件著作权5项。提出过多种新型群集智能算法并成功地将所提出算法应用于多种实际优化问题,提出的一系列进化算法标准测试函数集被全世界67个国家和地区45个学科领域的学者认可和使用。


Published papers

  1. K. J. Yu, J. Liang, B. Y. Qu, Y. Luo, C. T. Yue. Dynamic Selection Preference-Assisted Constrained Multiobjective Differential Evolution[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi:10.1109/TSMC.2021.3061698.paper

  2. C. T. Yue, P. N. Suganthan, J. Liang, B. Y. Qu, K. J. Yu, Y. S. Zhu, Y. Li. Differential evolution using improved crowding distance for multimodal multiobjective optimization[J]. Swarm and Evolutionary Computation, 2021, 62: 100849.(paper

  3. X. J. Jia, J. J. Liang, K. Zhao, Z. L. Yang, M. Y. Yu, Multi-parameters optimization for electromagnetic acoustic transducers using surrogate-assisted particle swarm optimizer[J]. Mechanical Systems and Signal Processing, 152 (2021).107337:1-18. paper

  4. Y. Hu, B. Y. Qu, J. Wang, J. Liang, Y. L. Wang, K. J. Yu, Y. X. Li, K. J. Qiao. Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning[J]. Applied Energy, 285 (2021): 116415. (paper)

  5. C. T. Yue, J. J. Liang, P. N. Suganthan, B. Y. Qu, K. J. Yu, S. Liu. MMOGA for Solving Multimodal Multiobjective Optimization Problems with Local Pareto Sets[C]// 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020: 1-8. (paper

  6. J. Liang, G. Chen, B. Qu, K. Yu, C. Yue, K. Qiao, H. Qian. Cooperative co-evolutionary comprehensive learning particle swarm optimizer for formulation design of explosive simulant[J]. Memetic Computing, 12 (2020): 331-341. paper

  7. J. Liang, K. Qiao, C. Yue, K. Yu, B. Qu, R. Xu, Z. Li, Y. Hu. A Clustering-Based Differential Evolution Algorithm for Solving Multimodal Multi-Objective Optimization Problems[J]. Swarm and Evolutionary Computation, 2020.(paper)(code)

  8. J. Liang, K. Qiao, K. Yu, S. Ge, B. Qu, R. Xu, K. Li. Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution[J]. Solar Energy, 2020, 207:336-346.paper)(code

  9. J. Liang, K. Qiao, M. Yuan, K. Yu, B. Qu, S. Ge, Y. Li, G. Chen. Evolutionary multi-task optimization for parameters extraction of photovoltaic models[J]. Energy Conversion and Management, 2020, 207: 112509.(paper

  10. Z. Cheng, Z. Li, J. Liang, J. Si, L. Dong, J. Gao. Distributed coordination control strategy for multiple residential solar PV systems in distribution networks[J]. International Journal of Electrical Power & Energy Systems, 2020, 117: 105660.paper

  11. C. Yue, J. Liang, B. Qu, Y. Han, Y. Zhu, O. D. Crisalle. A novel multiobjective optimization algorithm for sparse signal reconstruction[J]. Signal Processing, 2020, 167: 107292.paper

  12. J. Liang, S. Ge, B. Qu, K. Yu, F. Liu, H. Yang, P. Wei, Z. Li. Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models[J]. Energy Conversion and Management, 2020, 203: 112138.paper

  13. B. Qu, C. Li, J. Liang, L. Yan, K. Yu, Y. Zhu. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems[J]. Applied Soft Computing, 2020, 86: 105886.papercode

  14. B. Y. Qu, J. J. Liang, Y. S. Zhu, P. N. Suganthan. Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method[J]. Natural Computing, 2019, 18.4: 695-703.paper

  15. Y. Wang, B. Qu, J. Liang, Y. Wei, C. Yue, Y. Hu, H. Song. Two-Stage Decomposition Method Based on Cooperation Coevolution for Feature Selection on High-Dimensional Classification[J]. IEEE Access, 2019, 7: 163191-163201.paper

  16. J. Liang, H. Yang, J. Gao, C. Yue, S. Ge, B. Qu. MOPSO-Based CNN for Keyword Selection on Google Ads[J]. IEEE Access, 2019, 7: 125387-125400.paper

  17. Z. Li, Z. Cheng, J. Liang, J. Si, L. Dong, S. Li. Distributed event-triggered secondary control for economic dispatch and frequency restoration control of droop-controlled AC microgrids[J]. IEEE Transactions on Sustainable Energy, 2019.paper

  18. Jie Wang, Chaohao Zhao, Jing Liang, Caitong Yue, Xiangyang Ren and Ke Ba. Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification[C]// Bio-Inspired Computing: Theories and Applications, 2019. paper

  19. Jing Liang, Panpan Wei, Boyang Qu, Kunjing Yu, Caitong Yue, Yi Hu and Shilei Ge. Ensemble Learning Based on Multimodal Multiobjective Optimization[C]// Bio-Inspired Computing: Theories and Applications, 2019. paper

  20. Jie Wang, Bo Wang, Jing Liang, Kunjie Yu, Caitong Yue and Xiangyang Ren. Ensemble Learning via Multimodal Multiobjective Differential Evolution and Feature Selection[C]// Bio-Inspired Computing: Theories and Applications, 2019. paper

  21. Jing Liang, Zhimeng Li and Boyang Qu, Kunjie Yu, Kangjia Qiao and Shilei Ge. A Knee Point based NSGA-II Multi-objective Evolutionary Algorithm[C]// Bio-Inspired Computing: Theories and Applications, 2019. paper

  22. Jing Liang, Yaxin Li, Boyang Qu, Kunjie Yu and Yi Hu. Mutation Strategy Selection Based on Fitness Landscape Analysis : A Preliminary Study[C]// Bio-Inspired Computing: Theories and Applications, 2019. paper

  23. U. Ashraf, J. J. Liang, A. Akhtar, K. J. Yu, Y. Hu, C. T. Yue, A. M. Masood and M. Kashif. Meta-Heuristic Hybrid Algorithmic Approach for Solving Combinatorial Optimization Problem(TSP)[C]// Bio-Inspired Computing: Theories and Applications, 2019. paper

  24. K. J. Yu, S. L. Ge, B. Y. Qu, J. J. Liang. A Modified Particle Swarm Optimization for Parameters Identification of Photovoltaic Models[C]// 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 2634-2641.paper

  25. S. Cheng, H. Lu, Y. Guo, X. Lei, J. Liang, J. Chen, Y. Shi. Dynamic Multimodal Optimization: A Preliminary Study[C]// 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 279-285.paper

  26. C. T. Yue, J. J. Liang, B. Y. Qu, K. J. Yu, H. Song. Multimodal Multiobjective Optimization in Feature Selection[C]// 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 302-309.paper

  27. H. Song, A. K. Qin, P. W. Tsai, J. J. Liang. Multitasking Multi-Swarm Optimization[C]// 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 1937-1944.paper

  28. W. Zhang, G. Li, W. Zhang, J. Liang, G. G. Yen. A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization[J]. Swarm and Evolutionary Computation, 2019: 100569.(paper

  29. C. T. Yue, B. Y. Qu, K. J. Yu, J. J. Liang, X. D. Li. A Novel Scalable Test Problem Suite for Multimodal Multiobjective Optimization[J]. Swarm and Evolutionary Computation, 2019, 48. 62-71. paper)(code)

  30. Q. K Pan, L. Gao, L. Wang, J. Liang, X. Y. Li. Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem[J]. Expert Systems with Applications, 2019, 124: 309-324.(paper

  31. Yu K, Qu B, Yue C, Ge S, Chen X, Liang J. A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module[J]. Applied energy, 2019, 237: 241-257.paper)(code)

  32. Z. Cheng, Z. Li, J. Liang, J. Gao, J. Si, S. Li. Distributed Economic Power Dispatch and Bus Voltage Control for Droop-Controlled DC Microgrids[J]. Energies, 2019, 12(7): 1400.(paper

  33. P. Wang, L. Guo, B. Qu, C. Yue, K. Yu, Y. Wang. Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution[J]. Memetic Computing, 2019: 1-16.(paper

  34. J. Liang, W. Xu, C. Yue, K. Yu, H. Song, O. D. Crisalle, B. Qu. Multimodal multiobjective optimization with differential evolution[J]. Swarm and Evolutionary Computation, 2019, 44: 1028-1059.paper)(code)

  35. Y. Hu, J. Wang, J. Liang, K. Yu, H. Song, Q. Guo, C. Yue, Y. Wang. A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm[J]. Science China Information Sciences, 2019, 62(7): 70206.paper)(code

  36. K. Yu, J. J. Liang, B. Y. Qu, Z. Cheng, H. Wang. Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models[J]. Applied Energy, 2018: 408-422.paper)(code

  37. K. Yu, L. While, M. Reynolds, X. Wang, J. J. Liang, L. Zhao, Z. Wang. Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization[J]. Energy, 2018, 148.(paper

  38. C. T. Yue, B.Y. Qu, J.J. Liang. A Multi-objective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multi-objective Problems[J]. IEEE Transactions on Evolutionary Computation, 2017, PP(99):1-1.(paper)code

  39. K. Yu, J. J. Liang, B. Y. Qu, X. Chen, H. Wang. Parameters identification of photovoltaic models using an improved JAYA optimization algorithm[J]. Energy Conversion & Management, 2017, 150:742-753.(paper)code

  40. B. Y. Qu, Q. Zhou, J. M. Xiao, J. J. Liang, P. N. Suganthan. Large Scale Portfolio Optimization Using Multi-objective Evolutionary Algorithms and Pre-selection Methods[J]. Mathematical Problems in Engineering,2017,(2017-02-20), 2017, 2017(6):1-14.(paper)

  41. B. Y. Qu, Y. S. ZhuY. C. Jiao, M. Y. Wu, P. N. Suganthan, J. J. Liang. A Survey on Multi-objective Evolutionary Algorithms for the Solution of the Environmental/Economic Dispatch Problems[J]. Swarm & Evolutionary Computation, 2017.(paper)

  42. B. Y. Qu, J. J. Liang, Y. S. Zhu, P. N. Suganthan. Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method[J]. Natural Computing, 2017:1-9.(paper)

  43. B. Y. Qu, J. J. Liang, Y. S. Zhu, Z. Y. Wang, P. N. Suganthan. Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm[J]. Information Sciences An International Journal, 2016, 351:48-66.(paper)

  44. B. Y. Qu, J. J. Liang, Z. Y. Wang, Q. Chen, P. N. Suganthan. Novel benchmark functions for continuous multimodal optimization with comparative results[J]. Swarm & Evolutionary Computation, 2016, 26:23-34.(paper)

  45. X. Chu, B. Niu, J.J. Liang, Q. Lu. An orthogonal-design hybrid particle swarm optimiser with application to capacitated facility location problem[J]. International Journal of Bio-Inspired Computation (IJBIC), 2016, 8(5):268.(paper)

  46. B. Y. Qu, B. F. Lang, J. J. Liang, A. K. Qin, O. D. Crisalle. Two-hidden-layer extreme learning machine for regression and classification[J]. Neurocomputing, 2015, 175:826-834.(paper)

  47. L. L. Wu, Q. H. Zhou, T. J. Chen, J. J. Liang, X. Wu. Application of particle swarm optimization method to incoherent scatter radar measurement of ionosphere parameters[J]. Journal of Geophysical Research Space Physics, 2015, 120(9):8096-8110.(paper)

  48. J. J. Liang, H. Song, B. Y. Qu. Comparison of Three Different Curves Used in Path Planning Problems Based on Particle Swarm Optimizer[J]. Mathematical Problems in Engineering, 2014, 2014(4):1-15.(paper)

  49. J. J. Liang, B. Y. Qu, X. B. Mao, B. Niu, D.Y. Wang. Differential Evolution Based on Fitness Euclidean-Distance Ratio for Multimodal Optimization[J]. Neurocomputing, 2014, 137(15):252-260.(paper)

  50. Y. Y. Han, J. J. Liang, Q. K. Pan, J. Q. Li, H. Y. Sang, N. N. Cao. Effective hybrid discrete artificial bee colony algorithms for the total flowtime minimization in the blocking flowshop problem[J]. International Journal of Advanced Manufacturing Technology, 2013, 67(1-4):397-414.(paper)

  51. B. Y. Qu, P. N. Suganthan, J. J. Liang. Differential Evolution With Neighborhood Mutation for Multimodal Optimization[J]. IEEE Transactions on Evolutionary Computation, 2012, 16(5):601-614.(paper)

  52. B. Y. Qu, J. J. Liang, P. N. Suganthan. Niching particle swarm optimization with local search for multi-modal optimization[J]. Information Sciences, 2012, 197(197):131-143.(paper)

  53. K. Z. Gao, Q. K. Pan, J. Q. Li, Y. T. Wang, J. J. Liang, A Hybrid Harmony Search Algorithm For The No-Wait Flow-Shop Scheduling Problems[J]. Asia-Pacific Journal of Operational Research, 2012, 29(02):1219-1419. (paper)

  54. J. J. Liang, Q. K. Pan, C. T. Jun, L. Wang. Solving the blocking flow shop scheduling problem by a dynamic multi-swarm particle swarm optimizer[J]. International Journal of Advanced Manufacturing Technology, 2011, 55(5-8):755-762.(paper)

  55. Q. K. Pan, P. N. Suganthan, J. J. Liang, M. FatihTasgetiren. A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem[J]. Expert Systems with Applications An International Journal, 2011, 38(4):3252-3259.(paper)

  56. Q. K. Pan, P. N. Suganthan, J. J. Liang , M. FatihTasgetiren. A local-best harmony search algorithm with dynamic subpopulations[J]. Engineering Optimization, 2010, 42(2):101-117.(paper)

  57. Q. K. Pan, P. N. Suganthan, M. FatihTasgetiren, J.J. Liang. A self-adaptive global best harmony search algorithm for continuous optimization problems[J]. Applied Mathematics & Computation, 2010, 216(3):830-848.(paper)

  58. V. L. Huang, P. N. Suganthan, J. J. Liang, C. C. Chan. Improving the performance of a FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer[J]. Proceedings of SPIE - The International Society for Optical Engineering, 2007, 1(8):373-378.(paper)

  59. J. J. Liang, P. N. Suganthan, C. C. Chan, V. L. Huang. Wavelength detection in FBG sensor network using tree search DMS-PSO[J]. IEEE Photonics Technology Letters, 2006, 18(12):1305-1307.(paper)

  60. J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3):281-295.(paper)code

  61. V. L. Huang, P. N. Suganthan, J. J. Liang. Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems[J]. International Journal of Intelligent Systems, 2006, 21(2):209–226.(paper)

  62. J. J. Liang, S. Baskar, P. N. Suganthan, A. K. Qin.Performance Evaluation of Multiagent Genetic Algorithm[J]. Natural Computing, 2006, 5(1):83-96.(paper)

  63. S. Baskar, A. Alphones, P. N. Suganthan, J. J. Liang. Design of Yagi-Uda antennas using comprehensive learning particle swarm optimisation[J]. IEE Proceedings - Microwaves, Antennas and Propagation, 2005, 152(5):340-346.(paper)