| Particle swarm optimization for traveling salesman problem KP Wang, L Huang, CG Zhou, W Pang Proceedings of the 2003 international conference on machine learning and …, 2003 | 606 | 2003 |
| Fuzzy discrete particle swarm optimization for solving traveling salesman problem W Pang, K Wang, C Zhou, L Dong The Fourth International Conference onComputer and Information Technology …, 2004 | 246 | 2004 |
| A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data J Ji, X Han, C Zhou, Z Wang Knowledge-Based Systems, 2012 | 178 | 2012 |
| Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations C Luo, W Pang, Z Wang, C Lin 2014 IEEE International Conference on Data Mining, 917-922, 2014 | 134 | 2014 |
| Combinatorial stresses kill pathogenic Candida species D Kaloriti, A Tillmann, E Cook, M Jacobsen, T You, M Lenardon, L Ames, ... Medical Mycology, 1-11, 2012 | 91 | 2012 |
| Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers Y Xue, T Tang, W Pang, AX Liu Applied Soft Computing 88, 106031, 2020 | 88 | 2020 |
| Modified particle swarm optimization based on space transformation for solving traveling salesman problem W Pang, KP Wang, CG Zhou, LJ Dong, M Liu, HY Zhang, JY Wang Proceedings of 2004 International Conference on Machine Learning and …, 2004 | 85 | 2004 |
| Hybrid Approach Based on Ant Algorithm for Solving Traveling Salesman Problem W Huang, L. Wang, K.P. Zhou, C.G. Yuang, Y. Pang Acta Scientiarium Naturalium Universitatis Jilinensis 40 (4), 369-373, 2002 | 81* | 2002 |
| Particle swarm optimization for traveling salesman problems L Huang, K Wang, C Zhou, W Pang, L Dong Acta Scientiarum Naturalium Universitatis Jilinensis 41 (4), 477-480, 2003 | 55 | 2003 |
| Deep ensemble learning for human action recognition in still images X Yu, Z Zhang, L Wu, W Pang, H Chen, Z Yu, B Li Complexity 2020, 1-23, 2020 | 46 | 2020 |
| Gans-based data augmentation for citrus disease severity detection using deep learning Q Zeng, X Ma, B Cheng, E Zhou, W Pang IEEE Access 8, 172882-172891, 2020 | 44 | 2020 |
| Deepswarm: Optimising convolutional neural networks using swarm intelligence E Byla, W Pang Advances in Computational Intelligence Systems: Contributions Presented at …, 2020 | 43 | 2020 |
| Semi-supervised clustering on heterogeneous information networks C Luo, W Pang, Z Wang Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia …, 2014 | 40 | 2014 |
| McDPC: multi-center density peak clustering Y Wang, D Wang, X Zhang, W Pang, C Miao, AH Tan, Y Zhou Neural Computing and Applications 32, 13465-13478, 2020 | 39 | 2020 |
| An evolutionary computation based feature selection method for intrusion detection Y Xue, W Jia, X Zhao, W Pang Security and Communication Networks 2018, 2018 | 38 | 2018 |
| Mammographic image classification with deep fusion learning X Yu, W Pang, Q Xu, M Liang Scientific Reports 10 (1), 14361, 2020 | 31 | 2020 |
| A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data ZM Jinchao Ji, Wei Pang, Yanlin Zheng, Zhe Wang PLOS ONE 10 (5), 2015 | 31 | 2015 |
| FREDPC: A feasible residual error-based density peak clustering algorithm with the fragment merging strategy MD Parmar, W Pang, D Hao, J Jiang, W Liupu, L Wang, Y Zhou IEEE Access 7, 89789-89804, 2019 | 30 | 2019 |
| A self-adaptive fireworks algorithm for classification problems Y Xue, B Zhao, T Ma, W Pang IEEE Access 6, 44406-44416, 2018 | 30 | 2018 |
| The social sciences and the web: From ‘Lurking’to interdisciplinary ‘Big Data’research J Bone, CD Emele, A Abdul, G Coghill, W Pang Methodological innovations 9, 2059799116630665, 2016 | 28 | 2016 |
George M. CoghillEmeritus Professor, University of AberdeenVerified email at abdn.ac.uk
