最优化理论与算法
发布时间：2022-01-16
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跟最优化相关的学术期刊：
INFORMS出版社：
Operations Research， https://pubsonline.informs.org/journal/opre
Management Science, https://pubsonline.informs.org/journal/mnsc
INFORMS Journal on Computing， https://pubsonline.informs.org/journal/ijoc
Springer出版社：
Mathematical Programming，https://www.springer.com/journal/10107
Mathematical Programming Computation， https://www.springer.com/journal/12532
Annals of Operations Research， https://www.springer.com/journal/10479
Journal of Optimization Theory and Applications, https://www.springer.com/journal/10957
Journal of Global Optimization，https://www.springer.com/journal/10898
Journal of Combinatorial Optimization, https://www.springer.com/journal/10878
Computational Optimization and Applications, https://www.springer.com/journal/10589
Optimization and Engineering, https://www.springer.com/journal/11081
Fuzzy Optimization and Decision Making, https://www.springer.com/journal/10700
Artificial Intelligence Review，https://www.springer.com/journal/10462
Journal of Heuristics, https://www.springer.com/journal/10732
The Journal of Supercomputing，https://www.springer.com/journal/11227
Neural Computing and Applications，https://www.springer.com/journal/521
Cognitive Computation, https://www.springer.com/journal/12559
Soft Computing, https://www.springer.com/journal/500
Applied Intelligence，https://www.springer.com/journal/10489
Complex & Intelligent Systems，https://www.springer.com/journal/40747
International Journal of Machine Learning and Cybernetics，https://www.springer.com/journal/13042
Engineering with Computers，https://www.springer.com/journal/366
Computing，https://www.springer.com/journal/607
Elsevier出版社：
European Journal of Operational Research，https://www.journals.elsevier.com/european-journal-of-operational-research
Omega, https://www.journals.elsevier.com/omega
Applied Mathematics and Computation，https://www.journals.elsevier.com/applied-mathematics-and-computation
Applied Mathematical Modelling, https://www.journals.elsevier.com/applied-mathematical-modelling
Information Sciences，https://www.sciencedirect.com/journal/information-sciences
Applied Soft Computing, https://www.sciencedirect.com/journal/applied-soft-computing
Neurocomputing， https://www.journals.elsevier.com/neurocomputing
Computers & Operations Research，https://www.journals.elsevier.com/computers-and-operations-research
Journal of Computational and Applied Mathematics， https://www.sciencedirect.com/journal/journal-of-computational-and-applied-mathematics
Knowledge-Based Systems, https://www.journals.elsevier.com/knowledge-based-systems
Decision Support Systems， https://www.journals.elsevier.com/decision-support-systems
Engineering Applications of Artificial Intelligence， https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence
Swarm and Evolutionary Computation， https://www.sciencedirect.com/journal/swarm-and-evolutionary-computation
Expert Systems with Applications, https://www.sciencedirect.com/journal/expert-systems-with-applications
Mathematics and Computers in Simulation, https://www.sciencedirect.com/journal/mathematics-and-computers-in-simulation
Advances in Engineering Software, https://www.sciencedirect.com/journal/advances-in-engineering-software
Theoretical Computer Science， https://www.sciencedirect.com/journal/theoretical-computer-science
SoftwareX, https://www.sciencedirect.com/journal/softwarex
IEEE出版社
IEEE Transactions on Automatic Control, https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9
IEEE Transactions on Evolutionary Computation， https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235
IEEE Transactions on Cybernetics， https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036
IEEE Transactions on Systems, Man, and Cybernetics: Systems， https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021
Evolutionary Computation， https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6720222
IEEE Access， https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639
SIAM出版社
SIAM Journal on Optimization， https://www.siam.org/publications/journals/siam-journal-on-optimization-siopt
SIAM Journal on Scientific Computing， https://www.siam.org/publications/journals/siam-journal-on-scientific-computing-sisc
SIAM Review， https://www.siam.org/publications/journals/siam-review-sirev
Taylor and Francis 出版社：
International Journal of Production Research， https://www.tandfonline.com/journals/tprs20
Engineering Optimization, https://www.tandfonline.com/journals/geno20
Optimization Methods and Software，https://www.tandfonline.com/journals/goms20
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.
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[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
无约束优化
[1] Liu D C, Nocedal J. On the limited memory BFGS method for large scale optimization[J]. Mathematical programming, 1989, 45(1): 503-528.
[2] Dai Y H, Yuan Y. A nonlinear conjugate gradient method with a strong global convergence property[J]. SIAM Journal on optimization, 1999, 10(1): 177-182.
[3] Kolda T G, Lewis R M, Torczon V. Optimization by direct search: New perspectives on some classical and modern methods[J]. SIAM review, 2003, 45(3): 385-482.
[4] Torczon V. On the convergence of pattern search algorithms[J]. SIAM Journal on optimization, 1997, 7(1): 1-25.
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[6] Wright S J. Coordinate descent algorithms[J]. Mathematical Programming, 2015, 151(1): 3-34.
[7] Nesterov Y. Efficiency of coordinate descent methods on huge-scale optimization problems[J]. SIAM Journal on Optimization, 2012, 22(2): 341-362.
约束优化
[2] Audet C, Dennis Jr J E. Mesh adaptive direct search algorithms for constrained optimization[J]. SIAM Journal on optimization, 2006, 17(1): 188-217.
[1] Birgin E G, Martínez J M, Raydan M. Nonmonotone spectral projected gradient methods on convex sets[J]. SIAM Journal on Optimization, 2000, 10(4): 1196-1211.
[3] Vandenberghe L, Boyd S. Semidefinite programming[J]. SIAM review, 1996, 38(1): 49-95.
[4] Byrd R H, Lu P, Nocedal J, et al. A limited memory algorithm for bound constrained optimization[J]. SIAM Journal on Scientific Computing, 1995, 16(5): 1190-1208.
[5] Byrd R H, Hribar M E, Nocedal J. An interior point algorithm for large-scale nonlinear programming[J]. SIAM Journal on Optimization, 1999, 9(4): 877-900.
[6] Duran M A, Grossmann I E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs[J]. Mathematical programming, 1986, 36(3): 307-339.
[7] Ben-Tal A, Nemirovski A. Robust convex optimization[J]. Mathematics of Operations Research, 1998, 23(4): 769-805.
[8] Bertsimas D, Brown D B, Caramanis C. Theory and applications of robust optimization[J]. SIAM Review, 2011, 53(3): 464-501.
[9] Androulakis I P, Maranas C D, Floudas C A. αBB: A global optimization method for general constrained nonconvex problems[J]. Journal of Global Optimization, 1995, 7(4): 337-363.
[10] Wächter A, Biegler L T. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming[J]. Mathematical programming, 2006, 106(1): 25-57.
[11] Luo Z Q, Ma W K, So A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20-34.
多目标优化
[1] Das I, Dennis J E. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems[J]. SIAM journal on optimization, 1998, 8(3): 631-657.
2.2 随机性优化算法
无约束优化
[1-1] X Zhou, C Yang, W Gui. State transition algorithm[J], Journal of Industrial and Management Optimization, 2012, 8 (4): 1039-1056. 
STA_2012.pdf
[1-2] Zhou X, Gao D Y, Yang C, et al. Discrete state transition algorithm for unconstrained integer optimization problems[J]. Neurocomputing, 2016, 173: 864-874. DSTA_2016.pdf
[1-3] 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
[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
[4-1] 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
[4-2] Dorigo M, Birattari M, Stutzle T. Ant colony optimization[J]. IEEE Computational Intelligence Magazine, 2006, 1(4): 28-39. ACO_2006.pdf
[5-1] 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
[5-2] 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
[5-3] 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. Stochastic Ranking for Constrained Evolutionary Optimization.pdf
[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. Theoretical and numerical constraint-handling.pdf
[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. Constraint-handling in genetic algorithms through the use of.pdf
[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. A generic framework for constrained optimization using genetic algorithms.pdf
[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. A multiobjective optimization-based evolutionary algorithm for constrained optimization.pdf
[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. Constrained optimization by applying the alpha constrained method to the nonlinear simplex method with mutations.pdf
[7] Zhang M, Luo W, Wang X. Differential evolution with dynamic stochastic selection for constrained optimization[J]. Information Sciences, 2008, 178(15): 3043-3074. Differential evolution with dynamic stochastic selection for constrained optimization.pdf
[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. An adaptive tradeoff model for constrained evolutionary optimization.pdf
[9] Mallipeddi R, Suganthan P N. Ensemble of constraint handling techniques[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(4): 561-579. Ensemble of Constraint Handling Techniques.pdf
[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. Constraint-handling in nature-inspired numerical optimization past, present and future.pdf
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