郑州大学计算智能实验室

Computational Intelligence Laboratory

Jing Liang (Director)


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 Jing Liang, Professor

E-mail:liangjing@zzu.edu.cn




Jing Liang received the B.E. degree from Harbin Institute of Technology, China and the Ph.D. degree from Nanyang Technological University, Singapore, and her dissertation received IEEE CIS Outstanding PhD dissertation award. She is currently a Professor of Zhengzhou University, Associate Editor of IEEE Computational Intelligence Magazine(2012-2017), Swarm and Evolutionary Computation(2015-),IEEE Transactions on Evolutionary Computation(2017-), Excutive Editor of Journal of Zhengzhou University (Engineering Science). Her main research interests are evolutionary computation, swarm intelligence optimization algorithm, differential evolution, multi-objective optimization, multi-modal optimization and neural network. She has published about 120 papers in computational intelligence area, which are cited more than 12000 times according to Google citation and her h-index is 39.


Published papers

  1. LIANG, Jing, QIAO, Kangjia, YU Kunjie,  et al. Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution[J]. Solar Energy, 2020, 207:336-346.paper)(code

  2. LIANG, Jing, et al. Evolutionary multi-task optimization for parameters extraction of photovoltaic models. Energy Conversion and Management, 2020, 207: 112509.(paper

  3. CHENG, Zhiping, et al. Distributed coordination control strategy for multiple residential solar PV systems in distribution networks. International Journal of Electrical Power & Energy Systems, 2020, 117: 105660.(paper

  4. YUE, Caitong, et al. A novel multiobjective optimization algorithm for sparse signal reconstruction. Signal Processing, 2020, 167: 107292.(paper

  5. LIANG, Jing, et al. Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models. Energy Conversion and Management, 2020, 203: 112138.(paper

  6. QU, Boyang, et al. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems. Applied Soft Computing, 2020, 86: 105886.(paper

  7. QU, B. Y., et al. Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method. Natural Computing, 2019, 18.4: 695-703.(paper

  8. WANG, Yanli, et al. Two-Stage Decomposition Method Based on Cooperation Coevolution for Feature Selection on High-Dimensional Classification. IEEE Access, 2019, 7: 163191-163201.(paper

  9. LIANG, Jing, et al. MOPSO-Based CNN for Keyword Selection on Google Ads. IEEE Access, 2019, 7: 125387-125400.(paper

  10. LI, Zhongwen, et al. Distributed event-triggered secondary control for economic dispatch and frequency restoration control of droop-controlled AC microgrids. IEEE Transactions on Sustainable Energy, 2019.(paper

  11. YU, K. J., et al. A Modified Particle Swarm Optimization for Parameters Identification of Photovoltaic Models. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 2634-2641.(paper

  12. CHENG, Shi, et al. Dynamic Multimodal Optimization: A Preliminary Study. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 279-285.(paper

  13. YUE, C. T., et al. Multimodal Multiobjective Optimization in Feature Selection. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 302-309.(paper

  14. SONG, Hui, et al. Multitasking Multi-Swarm Optimization. In:2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. p. 1937-1944.(paper

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

  16. 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, Swarm and Evolutionary Computation,  vol. 48, pp. 62-71, 2019. paper)(code)

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

  18. 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)

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

  20. Liang J, Wang P, Guo L, et al. Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution[J]. Memetic Computing, 2019: 1-16.(paper

  21. Liang J, Xu W, Yue C, et al. Multimodal multiobjective optimization with differential evolution[J]. Swarm and Evolutionary Computation, 2019, 44: 1028-1059.paper(code)

  22. Hu Y, Wang J, Liang J, et al. A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm[J]. Science China Information Sciences, 2019, 62(7): 70206.paper)(code

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

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

  25. 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

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

  27. B.Y. Qu, Q. Zhou, J.M. Xiao, J.J. Liang, PN 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)

  28. B. Y. Qu, Y. S. ZhuY.C. Jiao, M.Y. Wu, P. N. Suganthan and 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)

  29. B. Y. Qu, J. J. Liang, Y. S. Zhu and 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)

  30. B. Y. Qu, J. J. Liang, Y. S. Zhu, Z. Y. Wang and 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(C):48-66.(paper)

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

  32. 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)

  33. 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)

  34. 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)

  35. 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)

  36. 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)

  37. Y. Y. Han, J. J. Liang, Q. K. Pan, et al. 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)

  38. 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)

  39. 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)

  40. 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)

  41. J. J. Liang, Q. K. Pan, T. Chen, et al. 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)

  42. 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)

  43. 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)

  44. 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)

  45. J. J. Liang, C. C. Chan, V. L. Huang, et al. 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)

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

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

  48. 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)

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

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