| A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization S Jiang, S Yang IEEE Transactions on Evolutionary Computation 21 (3), 329-346, 2017 | 367 | 2017 |
| A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization S Jiang, S Yang IEEE Transactions on evolutionary Computation 21 (1), 65-82, 2016 | 299 | 2016 |
| An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts S Jiang, S Yang IEEE transactions on cybernetics 46 (2), 421-437, 2016 | 245 | 2016 |
| Evolutionary dynamic multiobjective optimization: Benchmarks and algorithm comparisons S Jiang, S Yang IEEE transactions on cybernetics 47 (1), 198-211, 2016 | 222 | 2016 |
| Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization S Jiang, S Yang, X Yao, KC Tan, M Kaiser, N Krasnogor Newcastle University, 2018 | 167 | 2018 |
| Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization S Jiang, S Yang, X Yao, KC Tan, M Kaiser, N Krasnogor Newcastle University, 2018 | 160 | 2018 |
| Novel prediction strategies for dynamic multiobjective optimization Q Zhang, S Yang, S Jiang, R Wang, X Li IEEE Transactions on Evolutionary Computation 24 (2), 260-274, 2019 | 121 | 2019 |
| Scalarizing functions in decomposition-based multiobjective evolutionary algorithms S Jiang, S Yang, Y Wang, X Liu IEEE Transactions on Evolutionary Computation 22 (2), 296-313, 2017 | 100 | 2017 |
| Deep learning based prediction on greenhouse crop yield combined TCN and RNN L Gong, M Yu, S Jiang, V Cutsuridis, S Pearson Sensors 21 (13), 4537, 2021 | 94 | 2021 |
| Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes S Yang, S Jiang, Y Jiang Soft Computing 21 (16), 4677-4691, 2017 | 93 | 2017 |
| Evolutionary dynamic multi-objective optimisation: A survey S Jiang, J Zou, S Yang, X Yao ACM Computing Surveys 55 (4), 1-47, 2022 | 88 | 2022 |
| A scalable test suite for continuous dynamic multiobjective optimization S Jiang, M Kaiser, S Yang, S Kollias, N Krasnogor IEEE transactions on cybernetics 50 (6), 2814-2826, 2019 | 50 | 2019 |
| An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth B Alhnaity, S Kollias, G Leontidis, S Jiang, B Schamp, S Pearson Information Sciences 560, 35-50, 2021 | 49 | 2021 |
| Handling dynamic multiobjective optimization environments via layered prediction and subspace-based diversity maintenance Y Hu, J Zheng, S Jiang, S Yang, J Zou IEEE Transactions on Cybernetics 53 (4), 2572-2585, 2021 | 35 | 2021 |
| A framework of scalable dynamic test problems for dynamic multi-objective optimization S Jiang, S Yang 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain …, 2014 | 28 | 2014 |
| Dynamic multi-objective optimization algorithm based decomposition and preference Y Hu, J Zheng, J Zou, S Jiang, S Yang Information Sciences 571, 175-190, 2021 | 27 | 2021 |
| Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons Y Wang, J Yu, S Yang, S Jiang, S Zhao Swarm and Evolutionary Computation 50, 100559, 2019 | 26 | 2019 |
| Convergence versus diversity in multiobjective optimization S Jiang, S Yang International Conference on Parallel Problem Solving from Nature, 984-993, 2016 | 22 | 2016 |
| AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation S Jiang, H Li, J Guo, M Zhong, S Yang, M Kaiser, N Krasnogor Information Sciences 515, 365-387, 2020 | 21 | 2020 |
| On the use of hypervolume for diversity measurement of Pareto front approximations S Jiang, S Yang, M Li 2016 IEEE symposium series on computational intelligence (SSCI), 1-8, 2016 | 19 | 2016 |
Shengxiang YangProfessor of Computational Intelligence, De Montfort UniversityBestätigte E-Mail-Adresse bei dmu.ac.uk
