郑州大学计算智能实验室

Computational Intelligence Laboratory

会议论文

 The papers here are for faster dissemination and academic research convinence purpose only, and the copyright of the final papers belongs to the corresponding publishers !

‖ 2024

  • T. Zhang, Y. Bi, J. Liang, B. Xue, M. J. Zhang. Decomposition-based

    Multi-objective Genetic Programming for Feature Learning in Image

    Classification[C]//Proceedings of the Genetic and Evolutionary Computation

    Conference Companion. 2024: 555-558.  (paper)

  • H. Y. Lin, J. Liang*, C. T. Yue, Y. N. Wang. A Niching-Based Reproduction and

    Preselection-Based Multiobjective Differential Evolution for Multimodal

    Multiobjective Optimization[C]//2024 IEEE Congress on Evolutionary

    Computation (CEC). IEEE, 2024: 1-8.  (paper)

  • P. Chen,J. Liang* , K. J. Qiao, P. N. Suganthan. A Two-stage Evolutionary

    Framework For Multi-objective Optimization[C]//2024 IEEE Congress on

    Evolutionary Computation (CEC). IEEE, 2024: 1-7. (paper)

‖ 2023

  • P. Wang, B. Xue, J. Liang, M. J. Zhang. Dimensionality Reduction

    for Classification Using Divide-and-Conquer Based Genetic Programming[C]//

    2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024: 1-8.

     (paper)

  • X. X. Ban, J. Liang*, K. J. Yu, K. J. Qiao, Y. N. Wang, J. Z. Peng. 

    A Dynamic Exemplars Selection-based Differential Evolution Algorithm for 

    Constrained Multi-objective Optimization[C]//2024 7th International  

    Symposium on Autonomous Systems (ISAS). 2024: 1-8. (paper)

  • K. J. Qiao, J. Liang, Y. Bi, K. J. Yu, C. T. Yue, B. Y. Qu. An Unconstrained

    Auxiliary Framework for Constrained Many-Objective Optimization[C]//2023

    5th International Conference on Data-driven Optimization of Complex Systems

    (DOCS), IEEE, 2023: 1-8. (paper)

  • J. Liang, Z. Hu, Z. W. Li, Y. Bi, H. Cheng, W. F. Guo*. A Novel Evolutionary Constrained Multi-Objective Optimization Method for Identifying Personalized Drug Targets Combining with Structural Network Control Principles[C]//2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS), IEEE, 2023: 1-7. (paper)

  • J. Liang, X. D. Sui, C. T. Yue, M. Y. Yu. Differential Evolution Using

    Interpolation Strategy for Multimodal Multiobjective Optimization[C]//2023

    3rd International Symposium on Computer Technology and Information Science

    (ISCTIS), 2023: 764-769. (paper)

  • L. Y. Zhu, F. F. Zhang, X. D. Zhu, K Chen*, M. J. Zhang. Sample-Aware

    Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning

    in Dynamic Flexible Job Shop Scheduling[C]//Proceedings of the Genetic

    and Evolutionary Computation Conference, pp. 384-392, 2023.(paper)

‖ 2022

  • J. Liang, J. T. Yang, C. T. Yue, G.P Li, K. J. Yu and B. Y. Qu. A Multimodal Multiobjective Genetic Algorithm for Feature Selection[C]//2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022

‖ 2021

  • K. J. Qiao, J. J. Liang*, K. J. Yu, B. Y. Qu, C. T. Yue, and G. P. Li. A Self-Adaptive Multi-Task Differential Evolution Algorithm[C]// Asian Conference on Artificial Intelligence Technology, 2021.

  • J. J. Liang, H. Guo, K. J. Yu, B. Y. Qu, C. T. Yue, and K. J. Qiao*. An Improved Composite Differential Evolution with Self-Adaptive Mutation Strategy for Identifying Photovoltaic Model Parameters[C]// Asian Conference on Artificial Intelligence Technology, 2021.

  • J. J. Liang, L. Y. Zhang, K. J. Yu, B. Y. Qu, C. T. Yue, and K. J. Qiao*. A Differential Evolution Based Self-Adaptive Multi-Task Evolutionary Algorithm[C]// Asian Conference on Artificial Intelligence Technology, 2021. 

  • X. X. Ban, J. J. Liang*, K. J. Yu, K. J. Qiao, K. Li and Z. L. Chen. A Two-Stage Algorithm for Solving Constrained Multi-Objective Optimization Problems [C]// China Automation Congress, 2021.

  • Y. Hu, J. J. Liang*, B. Y. Qu, J. Wang, Y. L. Wang, and P. P. Wei. Evolutionary Ensemble Learning Using Multimodal Multi-Objective Optimization Algorithm Based on Grid for Wind Speed Forecasting [C]// 2021 IEEE Congress on Evolutionary Computation, pp. 1727-1734, 2021.(paper)

‖ 2020

  • K. J. Qiao, J. J. Liang, K. J. Yu, B. Y. Qu, C. T. Yue, and R. H. Xu. Parameter Extraction of the Photovoltaic Model via an Improved Composite Differential Evolution[C]// China Automation Congress, pp. 4868-4873, 2020.(paper)

  • J. J. Liang, P. S. Li, H. Zhao*, L. Han, and M. L. Qu. Forest Species Classification of UAV Hyperspectral Image Using Deep Learning[C]// China Automation Congress, pp. 7126-7130, 2020.(paper)

  • 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[C]// IEEE Congress on Evolutionary Computation, pp. 1-8, 2020.(paper)

‖ 2019

  • J. Wang, C. H. Zhao, J. J. Liang*, C. T. Yue, X. Y. Ren, and K. Ba. Chromosome Medial Axis Extraction Method Based on Graphic Geometry and Competitive Extreme Learning Machines Teams (CELMT) Classifier for Chromosome Classification[C]// International Conference on Bio-Inspired Computing: Theories and Applications, pp. 550-564, 2019.(paper)

  • J. J. Liang, P. P. Wei*, B. Y. Qu, K. J. Yu, C. T. Yue, Y. Hu, and S. L. Ge. Ensemble Learning Based on Multimodal Multiobjective Optimization[C]// International Conference on Bio-Inspired Computing: Theories and Applications, pp. 299-313, 2019.(paper)

  • J. Wang, B. Wang, J. J. Liang*, K. J. Yu, C. T. Yue, and X. Y. Ren. Ensemble Learning via Multimodal Multiobjective Differential Evolution and Feature Selection[C]// International Conference on Bio-Inspired Computing: Theories and Applications, pp. 439-453, 2019.(paper)

  • J. J. Liang, Z. M. Li*, B. Y. Qu, K. J. Yu, K. J. Qiao, and S. L. Ge. A Knee Point based NSGA-II Multi-Objective Evolutionary Algorithm[C]// International Conference on Bio-Inspired Computing: Theories and Applications, pp. 454-467, 2019.(paper)

  • J. J. Liang, Y. X. Li, B. Y. Qu, K. J. Yu, and Y Hu. Mutation Strategy Selection Based on Fitness Landscape Analysis: A Preliminary Study[C]// International Conference on Bio-Inspired Computing: Theories and Applications, pp. 284-298, 2019.(paper)

  • 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]// International Conference on Bio-Inspired Computing: Theories and Applications, pp. 622-633, 2019.(paper)

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

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

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

  • J. J. Liang, H. T. Yang, W. T. Sun, and J. J. Gao. PSO-Based CNN for Keyword Selection on Google Ads[C]// IEEE Congress on Evolutionary Computation, pp. 562-569, 2019.(paper)

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

‖ 2018

  • J. J. Liang, P. Wang, C. T. Yue, K. J. Yu, Z. H. Li, and B. Y. Qu. Multi-Objective Brainstorm Optimization Algorithm for Sparse Optimization[C]// IEEE Congress on Evolutionary Computation, pp. 1-8, 2018.(paper)

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

  • 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[C]// International Conference on Swarm Intelligence, pp. 550-560, 2018.(paper)(code)

‖ 2017

  • 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[C]// International Conference on Extreme Learning Machines, pp. 12-21, 2017.(paper)

  • M. Y. Yu*, J. J. Liang, B. Y. Qu, and C. T. Yue. Optimization of UWB Antenna Based on Particle Swarm Optimization Algorithm[C]// International Symposium on Intelligence Computation and Applications, pp. 86-97, 2017.(paper)

  • J. J. Liang, M. Y. Yu, C. T. Yue, M. M. Li, and Z. X. Yue. Routing Algorithm Based on SPSO[C]// Electronic and Automation Control Conference, pp. 1350-1354, 2017.(paper)

  • B. L. Li, J. J. Liang*, C. T. Yue, and B. Y. Qu. Multivariant Optimization Algorithm with Bimodal-gauss[C]// International Conference on Simulated Evolution and Learning, pp. 920-928, 2017.(paper)

  • 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[C]// International Conference on Simulated Evolution and Learning, pp. 911-919, 2017.(paper)

‖ 2016

  • J. J. Liang, C. T. Yue, and B. Y. Qu. Multimodal Multi-Objective Optimization: A preliminary study[C]// IEEE Congress on Evolutionary Computation, pp. 2454-2461, 2016.(paper)

  • Y. Jiao, L. Yang, B. Y. Qu*, D. Liu, J. J. Liang, and J. Xiao. Novel Local Particle Swarm Optimizer for Multimodal Optimization[C]// International Conference in Swarm Intelligence, pp. 571-578, 2016.(paper)

‖ 2015

  • L. L. Wu*, J. J. Liang, T. J. Chen, Y. Q. Wang, and O. D. Crisalle. DMS-PSO Based Tuning of PI Controllers for Frequency Control of a Hybrid Energy System[C]// International Conference in Swarm Intelligence, pp. 208-215, 2015.(paper)

  • J. J. Liang, L. Guo, R. Liu, and B.Y. Qu. A Self-Adaptive Dynamic Particle Swarm Optimizer[C]// IEEE Congress on Evolutionary Computation, pp. 3206 -3213, 2015.(paper)

  • 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[C]// IEEE Congress on Evolutionary Computation, pp. 3214-3219, 2015.(paper)

‖ 2014

  • J. J. Liang, H. Song, B. Y. Qu, W. Liu, and A. K. Qin. Neural Network Based on Dynamic Multi-Swarm Particle Swarm Optimizer for Ultra-Short-Term Load Forecasting[C]// International Conference on Swarm Intelligence, pp. 384-391, 2014.(paper)

  • B. Niu, H. Huang, B. Ye, L. Tan, and J. J. Liang. Fully Learned Multi-swarm Particle Swarm Optimization[C]// International Conference on Swarm Intelligence, pp. 150-157, 2014.(paper)

  • W. Liu, H. Song, J. J. Liang, B.Y. Qu, and A.K. Qin. Neural Network Based on Self-Adaptive Differential Evolution for Ultra-Short-Term Power Load Forecasting[C]// International Conference on Intelligent Computing, pp. 403-412, 2014.(paper)

  • B. Zheng, B. Y. Qu, J. J. Liang, and H. Song. Multi-Objective Comprehensive Learning Particle Swarm Optimization based on Summation of Normalized Objectives and Diversified Selection[C]// Chinese Control and Decision Conference, pp. 1339-1343, 2014.(paper)

  • B. Y. Qu, J. J. Liang, J. M. Xiao, and Z. G. Shang. Memetic Differential Evolution Based on Fitness Euclidean-Distance Ratio[C]// IEEE Congress on Evolutionary Computation, pp. 2266-2273, 2014.(paper)

  • 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[C]// IEEE Congress on Evolutionary Computation, pp. 445-450, 2014.(paper)

  • Z. H. Li, Z. G. Shang, J. J. Liang, and B. Y. Qu. Differential Evolution Strategy based on the Constraint of Fitness Values Classification[C]// IEEE Congress on Evolutionary Computation, pp. 1454-1460, 2014.(paper)

  • Z. H. Li, Z. G. Shang, J. J. Liang, and B. Y. Qu. Feature Selection based on Manifold-Learning with Dynamic Constraint-Handling Differential Evolution[C]// IEEE Congress on Evolutionary Computation, pp. 332-337, 2014.(paper)

‖ 2013

  • B. Niu, Q. Q. Duan, and J. J. Liang. Adaptive Bacterial Foraging Algorithm for Data Clustering[C]// International Conference on Intelligent Data Engineering and Automated Learning, pp. 577-584, 2013.

  • Min Niu, X. B. Mao, J. J. Liang, and Ben Niu. Object Tracking Based on Extended SUR Fand Particle Filter[C]// International Conference on Intelligent Computing, pp. 649–657, 2013.(paper)

  • C. Chen, J. J. Liang, B. Y. Qu, and B. Niu. Using Dynamic Multi-Swarm Particle Swarm Optimizer to Improve the Image Sparse Decomposition Based on Matching Pursuit[C]// International Conference on Intelligent Computing, pp. 587–595, 2013.(paper)

  • T. J. Chen, L.L. Wu, J. J. Liang, and Q. H. Zhou. Research and Analysis on Ionospheric Composition Based on Particle Swarm Optimization[C]//Intelligent Computing Theories and Technology, pp. 596-604, 2013.(paper)

  • B. Niu, H. L. Huang, L. J. Tan, and J. J. Liang. Multi-swarm Particle Swarm Optimization with a Center Learning Strategy[C]// International Conference on Swarm Intelligence, pp. 72–78, 2013.(paper)

  • J. J. Liang and B.Y. Qu. Large-scale Portfolio Optimization Using Multi-objective Dynamic Mutli-Swarm Particle Swarm Optimizer[C]// IEEE Symposium on Swarm Intelligence, pp. 1-6, 2013.(paper)

  • J. J. Liang, H. Song, and B.Y. Qu. Performance Evaluation of Dynamic Multi-Swarm Particle Swarm Optimizer with Different Constraint Handling Methods on Path Planning Problems[C]// IEEE Symposium Series on Computational Intelligence, pp. 65-71, 2013.(paper)

‖ 2012

  • 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 [C]// International Conference on Intelligent Computing, pp. 252-260, 2012.(paper)

  • J. J. Liang, H. Song, B. Y. Qu, and X. B. Mao. Path Planning Based on Dynamic Multi-Swarm Particle Swarm Optimizer with Crossover[C]// Intelligent Computing Theories and Applications, pp. 159-166, 2012.(paper)

  • J. J. Liang, W. X. Zhang, B. Y. Qu, and T. J. Chen. Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Environmental/Economic Dispatch Problem[C]// Intelligent Computing Technology, pp. 657-664, 2012.(paper)

  • Z. G. Shang, Z. H. Li, and J. J. Liang. Real Coded Feature Selection Integrated with Self-Adaptive Differential Evolution Algorithm[C]// Emerging Intelligent Computing Technology and Applications, pp. 481-488, 2012.(paper)

  • B. Y. Qu, J. J. Liang, P. N. Suganthan, and T. J. Chen. Ensemble of Clearing Differential Evolution for Multi-modal Optimization[C]// International Conference on Swarm Intelligence, pp. 350-357, 2012.(paper)

  • J. J. Liang, B. Y. Qu, P. N. Suganthan, and B. Niu. Dynamic Multi-Swarm Particle Swarm Optimization for Multi-Objective Optimization Problems[C]// IEEE Congress on Evolutionary Computation, pp. 605-612, 2012.(paper)

  • J. J. Liang, S. T. Ma, B. Y. Qu, and B. Niu. Strategy Adaptative Memetic Crowding Differential Evolution for Multimodal Optimization[C]// IEEE Congress on Evolutionary Computation, pp. 2885-2891, 2012.(paper)

  • J. J. Liang, X. B, Mao, B. Y Qu, B. Niu*, and T. J. Chen. Elite Multi-Group Differential Evolution[C]// IEEE Congress on Evolutionary Computation, pp. 1775-1781, 2012.(paper)

  • Z. G. Shang, Z. H. Li, J. J. Liang, and B. Niu. Control Parameters Self-Adaptation in Differential Evolution Based on Intrisic Structure Information[C]// IEEE Congress on Evolutionary Computation, pp. 349-354, 2012.(paper)

  • Z. H. Li, Z. G. Shang, J. J. Liang, and B. Niu. An Improved Differential Evolution for Constrained Optimization with Dynamic Constraint-Handling Mechanism[C]// IEEE World Congress on Computational Intelligence, pp. 343-348, 2012.(paper)

‖ 2011

  • J. J. Liang, B. Y. Qu，and S. T Ma. Memetic Fitness Euclidean-Distance Particle Swarm Optimization for Multi-modal Optimization[C]// International Conference on Intelligent Computing, pp. 378-385, 2011.(paper)

  • Liang, J. J. , &  Suganthan, P. N. . (2011). Dynamic multi-swarm particle swarm optimizer with harmony search[C]// IEEE Congress on Evolutionary Computation. IEEE.(paper)(code)

‖ 2010

  • J. J. Liang, Q. K. Pan, and T.J. Chen. A Dynamic Multi-Swarm Particle Swarm Optimizer for Blocking Flow Shop Scheduling[C]// International Conference on Bio-Inspired Computing: Theory and Applications, pp. 323-327, 2010.(paper)

  • J. J. Liang, Z. G. Shang, and Z. H. Li. Coevolutionary Comprehensive Learning Particle Swarm Optimizer[C]// IEEE Congress on Evolutionary Computation, pp. 1505-1512, 2010.(paper)

  • Y. Y Han, Q. K. Pan, J. J. Liang, and J. Q. Li. A Hybrid Discrete Harmony Search Algorithm For Blocking Flow Shop Scheduling[C]// International Conference on Bio-Inspired Computing: Theory and Applications, pp. 435-438, 2010.(paper)

  • W. J. Ren, Q. K. Pan, and J. J. Liang*. An Improved Harmony Search Algorithm for Multi-Dimensional Function Optimization Problem[C]// Bio-Inspired Computing: Theory and Applications, pp. 391-395, 2010.(paper)

  • W. J. Ren, Q. K. Pan, J. Q. Li, and J. J. Liang*. Tabu Search Algorithm for Solving No-idle Permutation Flow Shop Scheduling Problem[C]// Bio-Inspired Computing: Theory and Applications, pp. 449 – 453, 2010.(paper)

  • Z. H. Li, J. J. Liang, X. He, and Z. G. Shang. Differential Evolution with Dynamic Constraint-Handling Mechanism[C]// IEEE Congress on Evolutionary Computation, pp. 1899-1906, 2010.(paper)

  • J. H. Zhang, Z. G. Shang, J. J. Liang, L. Wang, and J. Yun. Analyzing Conformation of Fusion Protein on Bivalent Single-Chain Antibody with (Gly4Ser)n[C]// International Conference on Bioinformatics and Biomedical Engineering, pp. 1-4, 2010.(paper)

  • Q. K. Pan*, J. H. Duan, J. J. Liang, K. Z. Gao, and J. Q. Li. A Novel Discrete Harmony Search Algorithm for Scheduling Lot-Streaming Flow Shops[C]// Chinese Control and Decision Conference, pp. 1531-1536, 2010.(paper)

  • K. Z. Gao, H. Li, Q. K. Pan, J. Q. Li, and J. J. Liang. Hybrid Heuristics Based on Harmony Search to Minimize Total Flow Time in No-Wait Flow Shop[C]// Chinese Control and Decision Conference, pp. 1184-1188, 2010.(paper)

  • J. Q. Li*, Q. K. Pan, S. X. Xie, J. J. Liang, L. P. Zheng, and K. Z. Gao. A Hybrid Particle Swarm Optimization Algorithm for Bi-Criteria Flexible Job-Shop Scheduling Problem[C]// Chinese Control and Decision Conference, pp. 1537-1541, 2010.(paper)

  • J. Q. Li, Q. K. Pan, S. X. Xie, and J. J. Liang. A Hybrid Pareto-Based Tabu Search for Multi-Objective Flexible Job Shop Scheduling Problem with E/T Penalty[C]// Advances in Swarm Intelligence - First International Conference, pp. 620-627, 2010.(paper)

‖ 2009

  • F. R. Wang, W. H. Wang, Q. K. Pan, F. C. Zuo, and J. J. Liang. A Novel Online Test-Sheet Composition Approach for Web-Based Testing[C]// IEEE International Symposium on IT in Medicine & Education, pp. 700-705, 2009.(paper)

  • L. P. Zheng*, Q. K. Pan, G. Y. Li, and J. J. Liang. Improvement of Grayscale Image Segmentation Based on PSO Algorithm[C]// Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 442-446, 2009.(paper)

  • L. P. Zheng, G. Y. Li, J. J. Liang, and Q. K. Pan. Improved 2D Maximum Entropy Threshold Segmentation Method Based on PSO[C]// International Joint Conference on Computational Intelligence, pp. 287-291, 2009.(paper)

‖ 2008

  • S. Z. Zhao, J. J. Liang, P. N. Suganthan, and M. F. Tasgetiren. Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization[C]// Proceedings of IEEE Congress on Evolutionary Computation, pp. 3845-3852, 2008.(paper)(code)

‖ 2006

  • J. J. Liang and P. N. Suganthan. Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism[C]// IEEE Congress on Evolutionary Computation, pp. 9-16, 2006.(paper)(code)

  • J. J. Liang and P. N. Suganthan. Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning[C]// International Conference on Simulated Evolution and Learning, pp. 213-220, 2006.(paper)

‖ 2005

  • J. J. Liang, C. C. Chan, V. L. Huang, and P. N. Suganthan. Improving the Performance of a FBG Sensor Network Using a Novel Dynamic Multi-Swarm Particle Swarm Optimizer[C]// Proceedings of SPIE Symposium on Optics East, pp. 191-197, 2005.(paper)

  • J. J. Liang and P. N. Suganthan. Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search[C]// Proceedings of IEEE Congress on Evolutionary Computation, pp. 522-528, 2005.(paper)

  • J. J. Liang and P. N. Suganthan. Dynamic Multi-Swarm Particle Swarm Optimizer[C]// Proceedings of IEEE International Swarm Intelligence Symposium, pp. 124-129, 2005.(paper)

  • J. J. Liang, P. N. Suganthan, and K. Deb. Novel Composition Test Functions for Numerical Global Optimization[C]// Proceedings of IEEE International Swarm Intelligence Symposium, pp. 68-75, 2005.(paper)

‖ 2004

  • J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar. Particle Swarm Optimization Algorithms with Novel Learning Strategies[C]// International Conference on Systems, Man and Cybernetics, pp. 3659 - 3664, 2004.(paper)

  • J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar. Evaluation of Comprehensive Learning Particle Swarm Optimizer[C]// International Conference on Neural Information Processing, pp. 230-235, 2004.(paper)

  • K. Qin, P. N. Suganthan, and J. J. Liang. A New Generalized LVQ Algorithm Via Harmonic to Minimum Distance Measure Transition[C]// IEEE International Conference on Systems, Man and Cybernetics, pp. 4821 - 4825, 2004.(paper)



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郑州大学计算智能实验室