中文

Journal Publications

[1]X Zhou, J Tian, Nonlinear bilevel programming approach for decentralized supply chain using a hybrid state transition algorithm.Knowledge-Based Systems, 2022, 240
[2]Zeyu Wang, Xiaojun Zhou, Jituo Tian.Hierarchical parameter optimization based support vector regression for power load forecasting.Sustainable Cities and Society, 2021
[3]Xiaojun Zhou, Chaojie Li, Yuan Gao, Zhaoke Huang.A multiple gradient descent design for multi-task learning on edge computing: multi-objective machine learning approach.IEEE Transactions on Network Science and Engineering, 2021
[4]周晓君, 阳春华, 桂卫华.状态转移算法原理与应用[J].自动化学报, 2020, 46 (11) : 2260-2274.
[5]Jie Han, Xiaojun Zhou, Peng Shi, Cheng-Chew Lim, Chunhua Yang.Stackelberg-Nash game approach for constrained robust optimization with fuzzy variables[J].IEEE Transactions on Fuzzy Systems, 2020
[6]A hybrid feature selection method for production condition recognition in froth flotation with noisy labels[J].Minerals Engineering, 2020, 153 (106201)
[7]Hybrid intelligence assisted sample average approximation method for chance constrained dynamic optimization[J].IEEE Transactions on Industrial Informatics, 2020
[8]A fast optimization method with the speed of light, 2020
[9]X.J. Zhou, M Huang, T.W. Huang, C.H. Yang, W.H. Gui.Dynamic optimization for copper removal process with continuous production constraints[J].IEEE Transactions on Industrial Informatics, 2019
[10]W.H. Gui, X.J. Zhou, C.H. Yang, F.X. Zhang.Optimal setting and control strategy for industrial process based on discrete-time fractional-order PID[J].IEEE Access, 2019, 7: 47747--47761.
[11]S.X. Yang, X.J. Zhou, C.H. Yang, Z.K. Huang.Energy consumption forecasting for the nonferrous metallurgy industry using hybrid support vector regression with an adaptive state transition algorithm[J].Cognitive Computation, 2019
[12]T. W. Huang, C.H. Yang, Y.F. Xie, K. Yang, X.J. Zhou.A novel modularity-based discrete state transition algorithm for community detection in networks[J].Neurocomputing, 2019, 334: 89-99.
[13]G.B. Jia, C.C. Xu, J.P. Long, X.J. Zhou.An external archive-based constrained state transition algorithm for optimal power dispatch[J].Complexity, 2019, 4727168: 1-11.
[14]T.W. Huang, X.J. Zhou, C.H. Yang, Z.K. Huang.A hybrid feature selection method based on binary state transition algorithm and ReliefF[J].IEEE Journal of Biomedical and Health Informatics, 2019, 23 (5) : 1888--1898.
[15]W.H. Gui, C.H. Yang, X.J. Zhou.A statistical study on parameter selection of operators in continuous state transition algorithm[J].IEEE Transactions on Cybernetics, 2018, 49 (10) : 3722--3730.
[16]W.H. Gui, C.H. Yang, J.J. Zhou, X.J. Zhou.Set-point tracking and multi-objective optimization-Based PID control for the goethite process[J].IEEE ACCESS, 2018, 6: 36683-36698.
[17]W.H. Gui, R.D. Zhang, X.J. Zhou, J. Han, C.H. Yang.Discussion on uncertain optimization methods for nonferrous metallurgical processes[J].控制与决策, 2018, 33 (5) : 856--865.
[18]W.H. Gui, X.J. Zhou, C.H. Yang, Z.K. Huang.A novel cognitively-inspired state transition algorithm for solving the linear bi-level programming problem[J].Cognitive Computation, 2018, 10 (5) : 816–826.
[19]H.Q. Zhu, X.J. Zhou, C.H. Yang, F.X. Zhang.Fractional order fuzzy PID optimal control in copper removal process of zinc hydrometallurgy[J].Hydrometallurgy, 2018, 178: 60-76.
[20]W.H. Gui, C.H. Yang, T.W. Huang, X.J. Zhou*, M. Huang.Dynamic optimization based on state transition algorithm for copper removal process[J].Neural Computing and Applications, 2019, 31 (7) : 2827–2839.

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