教授
博士生导师
硕士生导师
入职时间：2014-12-23
所在单位：自动化学院
学历：博士研究生毕业
办公地点：中南大学校本部民主楼316
性别：男
联系方式：+86-13787052648
学位：博士学位
在职信息：在职
毕业院校：澳大利亚联邦大学
学科：控制科学与工程
人工智能
访问量：
最后更新时间：..
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[1]X Zhou, J Tian, Nonlinear bilevel programming approach for decentralized supply chain using a hybrid state transition algorithm.Knowledge-Based Systems, 2022, 240
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[2]Zeyu Wang, Xiaojun Zhou, Jituo Tian.Hierarchical parameter optimization based support vector regression for power load forecasting.Sustainable Cities and Society, 2021
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[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
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[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.
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[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
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[6]A hybrid feature selection method for production condition recognition in froth flotation with noisy labels[J].Minerals Engineering, 2020, 153 (106201)
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[7]Hybrid intelligence assisted sample average approximation method for chance constrained dynamic optimization[J].IEEE Transactions on Industrial Informatics, 2020
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[8]A fast optimization method with the speed of light, 2020
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[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
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[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.
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[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
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
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[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.
