周晓君

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

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最优化理论与算法

发布时间：2022-01-16

1、最优化理论

  1. Rockafellar R T. Convex analysis[M]. Princeton University Press, 1970.
  2. Boyd S, Boyd S P, Vandenberghe L. Convex optimization[M]. Cambridge University Press, 2004
  3. Bazaraa M S, Sherali H D, Shetty C M. Nonlinear programming: theory and algorithms[M]. John Wiley & Sons, 2006.
  4. Nocedal J, Wright S. Numerical optimization[M]. Springer Science & Business Media, 2006.
  5. Floudas C A. Nonlinear and mixed-integer optimization: fundamentals and applications[M]. Oxford University Press, 1995.
  6. Tawarmalani M, Sahinidis N V. Convexification and global optimization in continuous and mixed-integer nonlinear programming: theory, algorithms, software, and applications[M]. Springer Science & Business Media, 2002.
  7. Miettinen K. Nonlinear multiobjective optimization[M]. Springer Science & Business Media, 1999.
  8. Shapiro A, Dentcheva D, Ruszczynski A. Lectures on stochastic programming: modeling and theory[M]. Society for Industrial and Applied Mathematics, 2009.
  9. Ben-Tal A, El Ghaoui L, Nemirovski A. Robust optimization[M]. Princeton University Press, 2009.
  10. 袁亚湘. 非线性优化计算方法[M]. 科学出版社，2008.

2、最优化算法

2.1 确定性优化算法

     

2.2 随机性优化算法

无约束优化
  1. State transition algorithm（STA）
    1. X Zhou, C Yang, W Gui. State transition algorithm[J], Journal of Industrial and Management Optimization, 2012, 8 (4): 1039-1056. STA_2012.pdf
    2. Zhou X, Yang C, Gui W. A statistical study on parameter selection of operators in continuous state transition algorithm[J]. IEEE Transactions on Cybernetics, 2019, 49(10): 3722-3730. POSTA_2019.pdf
    3. X Zhou, C Yang, W Gui. State transition algorithm[J], Journal of Industrial and Management Optimization, 2012, 8 (4): 1039-1056. STA_2012.pdf
  2. Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing[J]. Science, 1983, 220(4598): 671-680. SA_1983.pdf
  3. Whitley D. A genetic algorithm tutorial[J]. Statistics and Computing, 1994, 4(2): 65-85. GA_1994.pdf
  4. Ant colony
    1. Dorigo M, Gambardella L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66. ACS_1997.pdf
    2. Dorigo M, Birattari M, Stutzle T. Ant colony optimization[J]. IEEE Computational Intelligence Magazine, 2006, 1(4): 28-39. ACO_2006.pdf
  5. Differential evolution（DE）
    1. Storn R, Price K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359. DE_1997.pdf
    2. Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE transactions on Evolutionary Computation, 2009, 13(2): 398-417. SaDE_2009.pdf
    3. Wang Y, Cai Z, Zhang Q. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 55-66. CoDE_2011.pdf
  6. Hansen N, Müller S D, Koumoutsakos P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)[J]. Evolutionary Computation, 2003, 11(1): 1-18. CMA-ES_2003.pdf
  7. Liang J J, Qin A K, Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295. CLPSO_2006.pdf
  8. Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[J]. Journal of global optimization, 2007, 39(3): 459-471. ABC_2007.pdf
  9. Yang X S, Deb S. Engineering optimisation by cuckoo search[J]. International Journal of Mathematical Modelling and Numerical Optimisation, 2010, 1(4): 330-343. CS_2010.pdf
  10. Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61. GWO_2014.pdf

 

约束优化
  1. Runarsson T P, Yao X. Stochastic ranking for constrained evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2000, 4(3): 284-294.
  2. Coello C A C. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art[J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191(11-12): 1245-1287.
  3. Coello C A C, Montes E M. Constraint-handling in genetic algorithms through the use of dominance-based tournament selection[J]. Advanced Engineering Informatics, 2002, 16(3): 193-203.
  4. Venkatraman S, Yen G G. A generic framework for constrained optimization using genetic algorithms[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(4): 424-435.
  5. Cai Z, Wang Y. A multiobjective optimization-based evolutionary algorithm for constrained optimization[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 658-675.
  6. Takahama T, Sakai S. Constrained optimization by applying the alpha constrained method to the nonlinear simplex method with mutations[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(5): 437-451.
  7. Zhang M, Luo W, Wang X. Differential evolution with dynamic stochastic selection for constrained optimization[J]. Information Sciences, 2008, 178(15): 3043-3074.
  8. Wang Y, Cai Z, Zhou Y, et al. An adaptive tradeoff model for constrained evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 80-92.
  9. Mallipeddi R, Suganthan P N. Ensemble of constraint handling techniques[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(4): 561-579.
  10. Mezura-Montes E, Coello C A C. Constraint-handling in nature-inspired numerical optimization: past, present and future[J]. Swarm and Evolutionary Computation, 2011, 1(4): 173-194.

 

多目标优化
  1. Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the strength Pareto evolutionary algorithm[J]. TIK-report, 2001, 103. SPEA2_2001.pdf
  2. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. NSGA-II_2002.pdf
  3. Deb K, Mohan M, Mishra S. Evaluating the ϵ-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions[J]. Evolutionary Computation, 2005, 13(4): 501-525. epsilon dominace -2005.pdf
  4. Zhang Q, Li H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731. MOEA-D_2007.pdf
  5. Bader J, Zitzler E. HypE: An algorithm for fast hypervolume-based many-objective optimization[J]. Evolutionary Computation, 2011, 19(1): 45-76. HypE_2011.pdf
  6. Yang S, Li M, Liu X, et al. A grid-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(5): 721-736. GrEA_2013.pdf
  7. Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601. NSGA-III_2014.pdf
  8. Zhang X, Tian Y, Jin Y. A knee point-driven evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2014, 19(6): 761-776. KnEA_2015.pdf
  9. Li K, Deb K, Zhang Q, et al. An evolutionary many-objective optimization algorithm based on dominance and decomposition[J]. IEEE Transactions on Evolutionary Computation, 2014, 19(5): 694-716. MOEA-DD-2015.pdf
  10. Cheng R, Jin Y, Olhofer M, et al. A reference vector guided evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5): 773-791. RVEA-2016.pdf

下一条：机器学习

机器学习

发布时间：2022-01-17

传统机器学习

  1. Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation[J]. Nature, 1986, 323: 533-536.
  2. Cortes C, Vapnik V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
  3. Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
  4. Breiman L. Random forests[J]. Machine learning, 2001, 45(1): 5-32.
  5. Rasmussen C E. Gaussian processes in machine learning[C]//Summer school on machine learning. Springer, Berlin, Heidelberg, 2003: 63-71.
  6. Smola A J, Schölkopf B. A tutorial on support vector regression[J]. Statistics and computing, 2004, 14(3): 199-222.
  7. Shlens J. A tutorial on principal component analysis[J]. arXiv preprint arXiv:1404.1100, 2014.
  8. Van der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9(11)：2579-2605.

深度学习

     

强化学习

     

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决策理论与方法

发布时间：2022-01-17

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流程工业智能制造

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待续。。

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能源互联网优化

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