B．Alhnaity, S. Kollias, G. Leontidis, S. Jiang, B. Schamp, S. Pearson, “An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth,” Information Sciences, 2021, accepted.
V. Cutsuridis, S. Jiang, M. J. Dunn, A. Rosser, J. Brawn, J.T. Erichsen, ``Neural modelling of antisaccade performance of healthy controls and early huntington’s disease patients,’’ Chaos, 2020, accepted.
Shouyong Jiang, Yong Wang, Marcus Kaiser, and Natalio Krasnogor, “NIHBA: A Network Interdiction Approach for Metabolic Engineering Design", Bioinformatics, https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa163/5804981; https://www.biorxiv.org/content/10.1101/752923v2.full.pdf
Shouyong Jiang, Hongru Li, Jinglei Guo, Mingjun Zhong, Shengxiang Yang, Marcus Kaiser, and Natalio Krasnogor, “ AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation”, Information Sciences, vol. 515, 365—387, 2020.
Qingyang Zhang, Shengxiang Yang, Shouyong Jiang, R. Wang, Xiaoli Li, “Novel prediction strategies for dynamic multi-objective optimization”, IEEE Transactions on Evolutionary Computation, 2019, in press.
Jinglei Guo, Yong Wu, W. Xie, Shouyong Jiang, “Triangular Gaussian mutation to differential evolution”, Soft Computing, pp.1—14, 2019.
Y. Wang, J. Yu, S. Yang, S. Jiang, and S. Zhao, “Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons”, Swarm and Evolutionary Computation, vol. 50, 2019, 100559.
Shouyong Jiang, Marcus Kaiser, Shengxiang Yang, Stefanos Kollias, Natalio Krasnogor: A Scalable Test Suite for Continuous Dynamic Multiobjective Optimisation. IEEE Transactions on Cybernetics, 2019. [link][C++]
Hong Li, Jinxing Hu, and Shouyong Jiang, “A hybrid PSO based on dynamic clustering for global optimization”, IFAC-PapersOnline, vol. 51, no. 8, pp. 269—274, 2018
Shouyong Jiang, Shengxiang Yang, Yong Wang, and Xiaobin Liu, “Scalarizing functions in decomposition- based evolutionary algorithms,” IEEE Transactions on Evolutionary Computation in press, 2018. [link] [C++]
Shouyong Jiang, Shengxiang Yang. A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, vol. 21, no. 1, pp. 65-82, 2017. [link] [C++]
Shouyong Jiang, Shengxiang Yang, "A strength Pareto evolutionary algorithm based on reference direction for multiobective and many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 21, no. 3, pp. 329-346, 2017. [link] [C++] [Matlab in PlatEMO]
Shouyong Jiang, Shengxiang Yang, “Evolutionary dynamic multi-objective optimization: benchmarks and algorithm comparisons,” IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 198—211, 2017. [link]
Shengxiang. Yang, Shouyong Jiang, and Yong Jiang, “Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes,” Soft Computing, vol. 21, no. 16, pp. 4677-4691, 2017.
Shouyong Jiang, Shengxiang Yang, “An improved multiobjective optimization evolutionary algorithm based on decomposition for complex Pareto fronts” IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 421–437, 2016. [link] [Matlab in PlatEMO]
W. Qian, H. Hou and S. Jiang, “New self-adaptive cuckoo search algorithm,” Computer Science, vol. 41, no. 7, pp.279-282, Jun. 2014.
S. Jiang, J. Guo, B. Alhnaity, and Q. Zhang, “On analysis of irregular Pareto front shapes,” EMO2021.
J. Guo, S. Jiang, et al., “An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme”, GECCO 2020.
M. Torres, S. Jiang, D. Pelta, M. Kaiser and N. Krasnogor, “Strain design as multiobjective network interdiction problem: A preliminary approach”, LNAI, CAEPIA 2018.
S. Jiang, M. Torres, D. Pelta, P. Krabben, R. Daniel, J.T. Luzardo, M. Kaiser, N. Krasnogor: Improving microbial strain design via multiobjective optimisation and decision making, in AI for sythetic biology 2, IJCIA, p.1-6, stockholm, sweden, July 2018
Marina Torres, Shouyong Jiang, David A. Pelta, Marcus Kaiser, Natalio Krasnogor: Strain Design as Multiobjective Network Interdiction Problem: A Preliminary Approach.CAEPIA 2018: 273-282
Shouyong Jiang, Marcus Kaiser, Shuzhen Wan, Jinglei Guo, Shengxiang Yang, Natalio Krasnogor: An Empirical Study of Dynamic Triobjective Optimisation Problems. CEC 2018: 1-8
Shouyong Jiang, Marcus Kaiser, Jinglei Guo, Shengxiang Yang, Natalio Krasnogor: Less detectable environmental changes in dynamic multiobjective optimisation. GECCO2018: 673-680
S.Jiang and S.Yang,“Convergence versus diversity in multiobjective optimization,” in 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), 2016, in press.
S. Jiang and S. Yang, “Adaptive penalty scheme for multiobjective evolutionary algorithm based on decomposition,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC 2016), 2016.
S. Jiang, and S. Yang, “On the use of Hypervolume for diversity measurement of Pareto front approximations,” in Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence in Multi-Criteria Decision Making (CIMCDM), 2016.
S. Jiang and S. Yang, “A fast strength Pareto evolutionary algorithm incorporating predefined preference information,” in Proceedings of the 15th UK Workshop on Computational Intelligence (UKCI), 2015.
S. Jiang and S. Yang, “Approximating multiobjective optimization problems with complex Pareto fronts,” in Proceedings of the 15th UK Workshop on Computational Intelligence (UKCI), 2015.
S. Jiang and S. Yang, “A framework of scalable dynamic test problems for dynamic multi-objective optimization,” in Proceedings of the 2014 IEEE Symposium Series on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2014, pp. 32-39
S. Jiang and S. Yang, “A benchmark generator for dynamic multi-objective optimization problems,” in Proceedings of the 14th UK Workshop on Computational Intelligence (UKCI), 2014, pp. 1-8.
S. Jiang and S. Yang, “An improved quantum-behaved particle swarm optimization based on linear interpolation,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), 2014, pp. 769-775.
S. Jiang, S. Yang, X. Yao and K. C. Tan. Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization. Technical Report, Newcastle University, U.K., January 2018 (PDF File).