| Differential evolution with multi-population based ensemble of mutation strategies G Wu, R Mallipeddi, PN Suganthan, R Wang, H Chen Information Sciences 329, 329-345, 2016 | 508 | 2016 |
| Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization G Wu, R Mallipeddi, PN Suganthan National University of Defense Technology, Changsha, Hunan, PR China and …, 2017 | 379 | 2017 |
| Ensemble of differential evolution variants G Wu, X Shen, H Li, H Chen, A Lin, PN Suganthan Information Sciences 423, 172-186, 2018 | 335 | 2018 |
| A test-suite of non-convex constrained optimization problems from the real-world and some baseline results A Kumar, G Wu, MZ Ali, R Mallipeddi, PN Suganthan, S Das Swarm and Evolutionary Computation 56, 100693, 2020 | 238 | 2020 |
| Ensemble strategies for population-based optimization algorithms–A survey G Wu, R Mallipeddi, PN Suganthan Swarm and evolutionary computation 44, 695-711, 2019 | 220 | 2019 |
| Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm PP Biswas, PN Suganthan, G Wu*, GAJ Amaratunga Renewable Energy, 2018 | 158 | 2018 |
| A two-phase scheduling method with the consideration of task clustering for earth observing satellites G Wu, J Liu, M Ma, D Qiu Computers & Operations Research 40 (7), 1884–1894, 2013 | 139 | 2013 |
| Problem definitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective real-parameter optimization G Wu, R Mallipeddi, P Suganthan Nanyang Technol. Univ., Singapore, Tech. Rep, 1-18, 2016 | 127 | 2016 |
| Hyperplane Assisted Evolutionary Algorithm for Many-Objective Optimization Problems H Chen, Y Tian, W Pedrycz, G Wu, R Wang, L Wang IEEE transactions on cybernetics, 2019 | 118 | 2019 |
| A variable reduction strategy for evolutionary algorithms handling equality constraints G Wu, W Pedrycz, PN Suganthan, R Mallipeddi Applied soft computing 37, 774-786, 2015 | 115 | 2015 |
| Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions X Wang, G Wu, L Xing, W Pedrycz IEEE Systems Journal, 2020 | 101 | 2020 |
| Across neighborhood search for numerical optimization G Wu Information Sciences 329, 597-618, 2016 | 99 | 2016 |
| Superior solution guided particle swarm optimization combined with local search techniques G Wu, D Qiu, Y Yu, W Pedrycz, M Ma, H Li Expert Systems with Applications 41 (16), 7536-7548, 2014 | 95 | 2014 |
| Global genetic learning particle swarm optimization with diversity enhancement by ring topology A Lin, W Sun, H Yu, G Wu, H Tang Swarm and evolutionary computation 44, 571-583, 2019 | 94 | 2019 |
| Two-echelon routing problem for parcel delivery by cooperated truck and drone Y Liu, Z Liu, J Shi, G Wu, W Pedrycz IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (12), 7450-7465, 2020 | 92 | 2020 |
| Coordinated planning of heterogeneous earth observation resources G Wu, W Pedrycz, H Li, M Ma, J Liu IEEE Transactions on Systems, Man, and Cybernetics: Systems 46 (1), 109-125, 2015 | 86 | 2015 |
| An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system R Wang, G Li, M Ming, G Wu, L Wang Energy 141, 2288-2299, 2017 | 84 | 2017 |
| On the effect of reference point in MOEA/D for multi-objective optimization R Wang, J Xiong, H Ishibuchi, G Wu, T Zhang Applied Soft Computing 58, 25-34, 2017 | 82 | 2017 |
| A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results A Kumar, G Wu, MZ Ali, Q Luo, R Mallipeddi, PN Suganthan, S Das Swarm and Evolutionary Computation 67, 100961, 2021 | 81 | 2021 |
| Multi-clustering via evolutionary multi-objective optimization R Wang, S Lai, G Wu, L Xing, L Wang, H Ishibuchi Information Sciences 450, 128-140, 2018 | 75 | 2018 |
Witold PedryczVerified email at ualberta.ca
