郑州大学计算智能实验室

Computational Intelligence Laboratory

Journal Article

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  1. 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, Mechanical Systems and Signal Processing, vol. 152, no. 2021, pp.107337:1-18. (paper

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

  3. Yue C, Suganthan P N, Liang J, et al. Differential evolution using improved crowding distance for multimodal multiobjective optimization[J]. Swarm and Evolutionary Computation, 2021, 62: 100849.paper

  4. Yi Hu, Boyang Qu, Jie Wang, Jing Liang*, et al. 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. J. J. Liang, Guanlin Chen, Boyang Qu, Kunjie Yu*, Caitong Yue, Kangjia Qiao, Hua Qian*. "Cooperative co-evolutionary comprehensive learning particle swarm optimizer for formulation design of explosive simulant." Memetic Computing, 12 (2020): 331-341. (paper

  6. J. Liang, K. J. Qiao, C. T. Yue, et al. A Clustering-Based Differential Evolution Algorithm for Solving Multimodal Multi-Objective Optimization Problems[J]. Swarm and Evolutionary Computation, 2020.(paper)(code)

  7. 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

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

  9. 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

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

  11. 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

  12. 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

  13. 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

  14. Zhu Y , Qiao B , Dong Y , et al. Multiobjective dynamic economic emission dispatch using evolutionary algorithm based on decomposition[J]. IEEJ Transactions on Electrical & Electronic Engineering, 2019, 14(9).1323-1333.paper)(code

  15. Zhang W, Li G, Zhang W, 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. 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

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

  18. 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

  19. 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)

  20. 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

  21. 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)

  22. 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

  23. 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

  24. Li Z, Shi L, Shang Z. Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems[J]. Swarm and Evolutionary Computation, 2019. paper

  25. 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)

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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)

  32. B. Y. Qu, Y. S. Zhu,Y.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)

  33. 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)

  34. 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)

  35. 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)

  36. 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)

  37. 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)

  38. 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)

  39. 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)

  40. 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)

  41. Qu B Y, Suganthan P N, Das S. A distance-based locally informed particle swarm model for multimodal optimization[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(3): 387-402.paper)(code

  42. 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)

  43. 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)

  44. 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)

  45. 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)

  46. 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)

  47. 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)

  48. 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)

  49. 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)

  50. 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)

  51. 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)

  52. 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

  53. 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)

  54. 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)

  55. 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)

  56. 许伟伟, 梁静, 岳彩通,. 多模态多目标差分进化算法求解非线性方程组[J]. 计算机应用研究, 2019(5).paper)(code

  57. 梁静, 宋慧, 王龙,瞿博阳. 多目标优化在中央空调节能优化系统中的应用[J]. 计算机仿真, 2015, 32(6):302-307.(paper)

  58. 梁静, 瞿博阳, 宋慧,刘巍. 电业超短期负荷预测仿真研究[J]. 计算机仿真, 2015, 32(7):96-101.(paper)

  59. 瞿博阳, 梁静, 王忠勇,郭丽. 模式识别双语教学中学生科研素质的提升[J]. 计算机教育, 2015(12):1-3.(paper)

  60. 梁静, 宋慧, 瞿博阳,毛晓波. 基于改进粒子群算法的路径优化问题研究[J]. 郑州大学学报(工学版), 2014, 35(1):34-38.(paper)

  61. 梁静, 宋慧, 瞿博阳. 多目标优化在路径优化中的应用[J]. 计算机仿真, 2014, 31(4):364-368.(paper

  62. 梁静,周钦亚,瞿博阳,宋慧. 基于混合策略的差分进化算法[J]. 郑州大学学报(工学版),2013, 34(5):59-62.(paper)

  63. 毛晓波, 梁静, 黄俊杰. 研究生智能仪器与仪表”课程教改探索[J]. 电气电子教学学报, 2012, 34(3):50-51.(paper)

  64. 韩红燕, 潘全科, 梁静. 改进的和声搜索算法在函数优化中的应用[J]. 计算机工程, 2010, 36(13):245-247.(paper)