郑州大学计算智能实验室

Jing Liang，Professor

Professor, School of Electrical Engineering, Zhengzhou University

Deputy Secretary and Vice President of Henan Institute of Technology

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 Transactions on Evolutionary Computation(2017-),  IEEE Transactions on Systems Man and Cybernetics: Systems (2021-Present), IEEE Computational Intelligence Magazine(2012-2017), Swarm and Evolutionary Computation(2015-)，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 more than 170 papers in computational intelligence area, which are cited more than 148700 times according to Google citation and her h-index is 41.

Representative Journal Papers

• J. J. Liang, P. N. Suganthan, A. K. Qin and S. Baska, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, vol. 10(3), pp. 281-295, 2006. (Highly cited)

• J. J. Liang, K. J. Qiao, K. J. Yu*, B. Y. Qu, C. T. Yue, W. F. Guo, L. Wang, Utilizing the relationship between unconstrained and constrained Pareto Fronts for constrained multi-Objective optimization, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2022.3163759, 2022.

• J. J. Liang, X. X. Ban, K. J. Yu*, B. Y. Qu, K. J. Qiao, C. T. Yue, K. Chen, K. C. Tan, A survey on evolutionary constrained multi-objective optimization, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2022.3155533, 2022.

• J. J. Liang, H. Y. Lin, C. T. Yue*, K. J. Yu, Y. Guo, K. J. Qiao, Multiobjective Differential Evolution with Speciation for Constrained Multimodal Multiobjective Optimization, IEEE Transactions on Evolutionary Computation, 10.1109/TEVC.2022.3194253, 2022.

• C. T. Yue, B. Y. Qu, and J. J. Liang*, A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems, IEEE Transactions on Evolutionary Computation, vol. 22, no. 5, pp. 805-817, 2018.(ESI Highly cited)

• K. J. Qiao, K. J. Yu, B. Y. Qu, J. J. Liang*, C. T. Yue, X. X. Ban. Feature Extraction for Recommendation of Constrained Multi-Objective Evolutionary Algorithms [J]. IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2022.3186667, 2022.

• K. J. Qiao, K. J. Yu, B. Y. Qu, J. J. Liang*, H. Song, C. T. Yue, H. Y. Lin, K. C. Tan, Dynamic auxiliary task-based evolutionary multitasking for constrained multi-objective optimization, IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2022.3175065, 2022.

• K. J. Qiao, K. J. Yu, B. Y. Qu, J. J. Liang*, H. Song, C. T. Yue, An evolutionary multitasking optimization framework for constrained multi-objective optimization problems, IEEE Transactions on Evolutionary Computation, 2022, 26(2): 263-277.

• B. Y. Qu, P. N. Suganthan and J. J. Liang, Differential evolution with neighborhood mutation for multimodal optimization, IEEE Transactions on Evolutionary Computation, vol. 6, no. 5, pp. 601-614, 2012.

• K. J. Yu, J. J. Liang*, B. Y. Qu, Y. Luo, C. T. Yue, Dynamic selection preference-assisted constrained multiobjective differential evolution, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2954-2965, 2021.

• K. J. Yu, J. J. Liang*, B. Y. Qu, C. T. Yue, Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization, Swarm and Evolutionary Computation, vol. 60, no. 2021, pp.100799:1-14.

• J. J. Liang*, Y. P. Wei, B. Y. Qu, C. T. Yue, H. Song, Ensemble learning based on fitness Euclidean-distance ratio differential evolution for classification. Natural Computing, 2020:1-11.

• 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.

• J. J. Liang, K. J. Qiao, C. T. Yue, K. J. Yu, B. Y. Qu, R. H. Xu, Z. M. Li, Y. Hu, A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems, Swarm and Evolutionary Computation, vol. 60, no. 100788, 2020.

• 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.

• J. J. Liang, K. J. Qiao, K. J. Yu, S. L. Ge, B. Y. Qu, R. H. Xu, K. Li, Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-base differential evolution, Solar Energy, 207(2020), pp.336-346.

• J. J. Liang, K. J. Qiao, M. H. Yuan, K. J. Yu, B. Y. Qu, S. L. Ge, Y. X. Li and G. L. Chen, Evolutionary multi-task optimization for parameters extraction of photovoltaic models, Energy Conversion and Management, 207(2020), pp.1125901-15, 2020.

• C. T. Yue, J. J. Liang*, B. Y. Qu, Y. H. Han, Y. S. Zhu and O. D. Crisalle, A novel multiobjective optimization algorithm for sparse signal reconstruction, Signal Processing, vol. 167, no. 2020, pp.107292-107304, 2020.

• C. T. Yue, B. Y. Qu, K. J. Yu, J. J. Liang* and X. D. Li, A novel scalable test problem suite for multimodal multiobjective optimization, Swarm Evolutionary and Computation. vol. 48, pp. 62-71, 2019.

• K. J. Yu, B. Y. Qu, C. T. Yue, S. L. Ge, X. Chen and J. J. Liang*, “A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module”, Applied Energy, vol. 237, no. 2019, pp. 241-257, 2019. (ESI Highly cited)

• Y. Hu, J. Wang, J. J. Liang*, K. J. Yu, H. Song, Q. Q. Guo, C. T. Yue and Y. L. Wang. A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm, SCIENCE CHINA Information Sciences, 62(5), pp. 070206:1-070206:17, 2019.

• J. J. Liang, S. Ge, B. Qu, K. Yu, F. Liu, H. Yang, P. Wei, and Z. Li, Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models, Energy Conversion and Management, pp. 112138, 2019.(ESI Highly cited)

• J. J. Liang, W. W. Xu, C. T. Yue, K. J. Yu, H. Song, O. C. Crisalle and B. Y. Qu*, Multimodal multiobjective optimization with differential evolution, Swarm and Evolutionary Computation, vol. 44, pp. 1028-1059, 2018.

• K. J. Yu, J. J. Liang*, B. Y. Qu, Z. P. Cheng and H. S. Wang, Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models, Applied Energy, vol. 226, no. 2018, pp. 408-422, 2018.(ESI Highly cited)

• B. Y. Qu, Y. S. Zhu, Y. C. Jiao, M. Y. Wu, J. J. Liang* and P. N. Suganthan, A Survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems, Swarm and Evolutionary Computation, vol. 38, pp. 1-11, 2018.

• K. J. Yu, J. J. Liang*, B. Y. Qu, X. Chen and H. S. Wang, Parameters identification of photovoltaic models using an improved JAYA optimization algorithm, Energy Conversion and Management, vol. 150, pp. 742-753, 2017.(ESI Highly cited)

• 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, Information Sciences, vol. 351, pp. 48-66, 2016.

• 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, Neurocomputing, vol. 175, pp. 826-834, 2016.

• B. Y. Qu, J. J. Liang*, Z. Y. Wang, Q. Chen and P. N. Suganthan, Novel benchmark functions for continuous multimodal optimization with comparative results, Swarm and Evolutionary Computation, vol. 26, pp. 23-34, 2016.

• J. J. Liang, B. Y. Qu, X. B. Mao, B. Niu and D.Y. Wang, Differential evolution based on fitness euclidean-distance ratio for multimodal optimization, Neurocomputing, vol. 137, pp. 252-260, 2014.

  B. Y. Qu, J. J. Liang and P. N. Suganthan, Niching particle swarm optimization with local search for multi-modal optimization, Information Sciences, vol. 197, pp. 131-143, 2012.

• J. J. Liang, C. C. Chan, P. N. Suganthan and V. L. Huang, Wavelength detection in FBG sensor network using tree search DMS-PSO, IEEE Photonics Technology Letters, vol. 18(12), pp. 1305 - 1307, 2006.

  Representative Conference Papers

• C. T. Yue, J. J. Liang, P. N. Suganthan, B. Y. Qu, K. J. Yu, and S. Liu, “MMOGA for Solving Multimodal Multiobjective Optimization Problems with Local Pareto Sets,” IEEE Congress on Evolutionary Computation, pp. 1-8, 2020.

• J. J. Liang, P. Wei, B. Qu, K. Yu, C. Yue, Y. Hu and S. Ge, “Ensemble Learning Based on Multimodal Multiobjective Optimization,” Bio-Inspired Computing: Theories and Applications, pp. 299-313, 2019.

• J. J. Liang, Z. Li and B. Qu, K. Yu, K. Qiao and S. Ge “A Knee Point based NSGA-II Multi-objective Evolutionary Algorithm” Bio-Inspired Computing: Theories and Applications, pp. 454-467, 2019.

• J. J. Liang, Y. Li, B. Qu, K. Yu and Y. Hu, “Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study,” Bio-Inspired Computing: Theories and Applications, pp. 284-298, 2019.

• J. J. Liang, H. T. Yang, W. T. Sun, J. J. Gao, “PSO-based CNN for Keyword Selection on Google Ads,” IEEE Congress on Evolutionary Computation, pp. 562-569, 2019.

• C. T. Yue, J. J. Liang, B. Y. Qu, K. J. Yu, and H. Song, “Multimodal Multiobjective Optimization in Feature Selection,” IEEE Congress on Evolutionary Computation, pp. 302-309, 2019.

• J. J. Liang, P. Wang, C. T. Yue, K. J. Yu, Z. H. Li, B. Y. Qu, Multi-objective Brainstorm Optimization Algorithm for Sparse Optimization. IEEE Congress on Evolutionary Computation, pp. 1-8, 2018.

• J. J. Liang, X. P. Zhu, C. T. Yue, Z. H. Li, B. Y. Qu, Performance Analysis on Knee Point Selection Methods for Multi-Objective Sparse Optimization Problems. IEEE Congress on Evolutionary Computation, pp. 2507-2514, 2018.

• J. J. Liang, Q. Q. Guo, C. T. Yue, B. Y. Qu and K. J. Yu, “A Self-Organizing Multi-Objective Particle Swarm Optimization Algorithm for Multimodal Multi-Objective Problems”, International Conference on Swarm Intelligence, pp. 550-560, 2018.

• C. T. Yue, J. J. Liang, B. Y. Qu, Z. P. Lu, B. L. Li, and Y. H. Han, “Sparse Representation Feature for Facial Expression Recognition”, International Conference on Extreme Learning Machines, pp. 12-21, 2017.

• M. Y. Yu, J. J. Liang, B. Y. Qu, and C. T. Yue, “Optimization of UWB Antenna Based on Particle Swarm Optimization Algorithm”, International Symposium on Intelligence Computation and Applications, pp. 86-97, 2017.

• J. J. Liang, M. Y. Yu,C. T. Yue, M. M. Li, and Z. X. Yue, “Routing Algorithm Based on SPSO, Advanced Information Technology”, Electronic and Automation Control Conference, pp. 1350-1354, 2017

• B. L. Li, J. J. Liang, C. T. Yue, and B. Y. Qu, “Multivariant Optimization Algorithm with Bimodal-gauss”, International Conference on Simulated Evolution and Learning 2017,pp. 920-92, 2017

• C. T. Yue, J. J. Liang, B. Y. Qu, H. Song, G. Li, and Y. H. Han, “A Knee Point Driven Particle Swarm Optimization Algorithm for Sparse Reconstruction”, International Conference on Simulated Evolution and Learning 2017, pp. 911-919, 2017

• J. J. Liang, C. T. Yue, and B. Y. Qu, “Multimodal multi-objective optimization: A preliminary study”, IEEE Congress on Evolutionary Computation 2016, pp. 2454-2461, 2016.

• J. J. Liang, L. Guo, R. Liu and B.Y. Qu, “A Self-adaptive Dynamic Particle Swarm Optimizer",IEEE Congress on Evolutionary Computation, pp. 3206 – 3213, 2015.

• B. Y. Qu, J. J. Liang, Z. Y. Wang and D. M. Liu, “Solving CEC 2015 Multi-modal Competition Problems Using Neighborhood Based Speciation Differential Evolution”, IEEE Congress on Evolutionary Computation, pp. 3214-3219, 2015.

• J. J. Liang, H. Song, B. Y. Qu, W. Liu & A. K. Qin, “Neural Network Based on Dynamic Multi-Swarm Particle Swarm Optimizer for Ultra-Short-Term Load Forecasting,” the Fifth International Conference on Swarm Intelligence(ICSI 2014), Advances in Swarm Intelligence, 384-391, 2014 ISSN:0302-9743

• B. Y. Qu, J. J. Liang, J. M. Xiao, and Z. G. Shang, “Memetic Differential Evolution Based on Fitness Euclidean-Distance Ratio”, IEEE Congress on Evolutionary Computation 2014, pp. 2266-2273, 2014.

• J. J. Liang, B. Zheng, B. Y. Qu, and H. Song, “Multi-objective Differential Evolution Algorithm Based on Fast Sorting and a Novel Constraints Handling Technique”, IEEE Congress on Evolutionary Computation 2014, pp. 445-450, 2014.