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Journal articles on the topic 'Optimization theory and applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Wang, Bin, Lotfollah Najjar, Neal N. Xiong, and Rung Ching Chen. "Stochastic Optimization: Theory and Applications." Journal of Applied Mathematics 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/949131.

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2

Yinyu, Ye, Jiang Bo, Li Xiaocheng, et al. "Modern optimization theory and applications." SCIENTIA SINICA Mathematica 50, no. 7 (2020): 899. http://dx.doi.org/10.1360/ssm-2020-0035.

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3

Consigli, Giorgio, Darinka Dentcheva, and Francesca Maggioni. "Stochastic optimization: theory and applications." Annals of Operations Research 292, no. 2 (2020): 575–80. http://dx.doi.org/10.1007/s10479-020-03672-y.

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4

Werner, Frank. "Discrete Optimization: Theory, Algorithms, and Applications." Mathematics 7, no. 5 (2019): 397. http://dx.doi.org/10.3390/math7050397.

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5

Bertsimas, Dimitris, David B. Brown, and Constantine Caramanis. "Theory and Applications of Robust Optimization." SIAM Review 53, no. 3 (2011): 464–501. http://dx.doi.org/10.1137/080734510.

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6

Chen, Bo, Peter Gritzmann, and Silvano Martello. "Combinatorial optimization: theory, computation, and applications." Discrete Applied Mathematics 217 (January 2017): 1–2. http://dx.doi.org/10.1016/j.dam.2016.10.011.

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7

Larsson, Torbjörn, Michael Patriksson, and Ann-Brith Strömberg. "Conditional subgradient optimization — Theory and applications." European Journal of Operational Research 88, no. 2 (1996): 382–403. http://dx.doi.org/10.1016/0377-2217(94)00200-2.

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8

Gauci, John Baptist, and Silvano Martello. "Theory and applications in combinatorial optimization." Journal of Combinatorial Optimization 42, no. 2 (2021): 207–11. http://dx.doi.org/10.1007/s10878-021-00785-3.

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9

El-Shorbagy, M. A., and Aboul Ella Hassanien. "Particle Swarm Optimization from Theory to Applications." International Journal of Rough Sets and Data Analysis 5, no. 2 (2018): 1–24. http://dx.doi.org/10.4018/ijrsda.2018040101.

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Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence. PSO is related to the study of swarms; where it is a simulation of bird flocks. It can be used to solve a wide variety of optimization problems such as unconstrained optimization problems, constrained optimization problems, nonlinear programming, multi-objective optimization, stochastic programming and combinatorial optimization problems. PSO has been presented in the literature and applied successfully in real life applications. In this paper, a comprehensive review of PSO as a well-known

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10

Tsai, Jung-Fa, John Gunnar Carlsson, Dongdong Ge, Yi-Chung Hu, and Jianming Shi. "Optimization Theory, Methods, and Applications in Engineering." Mathematical Problems in Engineering 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/759548.

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11

Luo, Gui-Mei, Xiaomin An, and Jian-Ye Xia. "Robust optimization with applications to game theory." Applicable Analysis 88, no. 8 (2009): 1183–95. http://dx.doi.org/10.1080/00036810903157196.

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12

Buhl, H. U. "Optimization of scrapping decisions: Theory and applications." Zeitschrift für Operations Research 32, no. 2 (1988): 111–19. http://dx.doi.org/10.1007/bf01919187.

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13

Meraihi, Yassine, Asma Benmessaoud Gabis, Seyedali Mirjalili, and Amar Ramdane-Cherif. "Grasshopper Optimization Algorithm: Theory, Variants, and Applications." IEEE Access 9 (2021): 50001–24. http://dx.doi.org/10.1109/access.2021.3067597.

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14

WANG, Long-Fei, and Le-Yuan SHI. "Simulation Optimization: A Review on Theory and Applications." Acta Automatica Sinica 39, no. 11 (2013): 1957. http://dx.doi.org/10.3724/sp.j.1004.2013.01957.

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15

Curtis, Michael A. "Optimization by Simulated Annealing Theory and Chemometric Applications." Journal of Chemical Education 71, no. 9 (1994): 775. http://dx.doi.org/10.1021/ed071p775.

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16

Tsai, Jung-Fa, John Gunnar Carlsson, Dongdong Ge, Yi-Chung Hu, and Jianming Shi. "Optimization Theory, Methods, and Applications in Engineering 2014." Mathematical Problems in Engineering 2015 (2015): 1–3. http://dx.doi.org/10.1155/2015/345858.

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17

Vongkulbhisal, Jayakorn, Fernando De la Torre, and Joao Paulo Costeira. "Discriminative Optimization: Theory and Applications to Computer Vision." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 4 (2019): 829–43. http://dx.doi.org/10.1109/tpami.2018.2826536.

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18

Yuan, Gonglin, Gaohang Yu, Neculai Andrei, Yunhai Xiao, and Li Zhang. "Nonlinear Analysis: Optimization Methods, Convergence Theory, and Applications." Abstract and Applied Analysis 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/429595.

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19

Battaglia, D. A., and L. Stella. "Optimization through quantum annealing: theory and some applications." Contemporary Physics 47, no. 4 (2006): 195–208. http://dx.doi.org/10.1080/00107510600861454.

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20

Fiig, Thomas, Karl Isler, Craig Hopperstad, and Peter Belobaba. "Optimization of mixed fare structures: Theory and applications." Journal of Revenue and Pricing Management 9, no. 1-2 (2009): 152–70. http://dx.doi.org/10.1057/rpm.2009.18.

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21

Tsai, Jung-Fa, John Gunnar Carlsson, Dongdong Ge, Yi-Chung Hu, and Jianming Shi. "Optimization Theory, Methods, and Applications in Engineering 2013." Mathematical Problems in Engineering 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/319418.

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22

WANG, Long-Fei, and Le-Yuan SHI. "Simulation Optimization: A Review on Theory and Applications." Acta Automatica Sinica 39, no. 11 (2013): 1957–68. http://dx.doi.org/10.1016/s1874-1029(13)60081-6.

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23

Boender, C. G. E., and Jonas Mockus. "Bayesian Approach to Global Optimization--Theory and Applications." Mathematics of Computation 56, no. 194 (1991): 878. http://dx.doi.org/10.2307/2008419.

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24

Consigli, Giorgio, Darinka Dentcheva, and Francesca Maggioni. "Correction to: Preface: Stochastic optimization: theory and applications." Annals of Operations Research 292, no. 2 (2020): 1001. http://dx.doi.org/10.1007/s10479-020-03727-0.

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25

Dür, Mirjam, Bülent Karasözen, Tamás Terlaky, and Gerhard-Wilhelm Weber. "Challenges of Continuous Optimization in Theory and Applications." European Journal of Operational Research 181, no. 3 (2007): 1065–68. http://dx.doi.org/10.1016/j.ejor.2006.03.033.

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26

Nicolau, Florentina, Witold Respondek, and Jean-Pierre Barbot. "Flat Inputs: Theory and Applications." SIAM Journal on Control and Optimization 58, no. 6 (2020): 3293–321. http://dx.doi.org/10.1137/19m127776x.

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27

Liu, Yan Hui. "Research of Particle Swarm Optimization Algorithm and its Applications in Optimization Theory." Advanced Materials Research 926-930 (May 2014): 3175–78. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3175.

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As everyone knows, mathematics is the basis of the optimization technology. With the development and progress of the society for a long time, there are many complicated practical problems during people’s research as well as some binding and nonlinear characteristics. If people still take the traditional optimization ways, such as Newton method and the simplex algorithm, they can not get rid of these problems effective particle swarm optimization algorithm and its applications in optimization theory in order to provide some useful references.

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28

Wang, Tiantian, Long Yang, and Qiang Liu. "Beetle swarm optimization algorithm: Theory and application." Filomat 34, no. 15 (2020): 5121–37. http://dx.doi.org/10.2298/fil2015121w.

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In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design pro

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29

Odu, G. O. "Review of Multi-criteria Optimization Methods – Theory and Applications." IOSR Journal of Engineering 3, no. 10 (2013): 01–14. http://dx.doi.org/10.9790/3021-031020114.

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30

Kwakernaak, Huibert. "H2-optimization — Theory and applications to robust control design." Annual Reviews in Control 26, no. 1 (2002): 45–56. http://dx.doi.org/10.1016/s1367-5788(02)80010-4.

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31

Brockhoff, Dimo, and Eckart Zitzler. "Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications." Evolutionary Computation 17, no. 2 (2009): 135–66. http://dx.doi.org/10.1162/evco.2009.17.2.135.

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Many-objective problems represent a major challenge in the field of evolutionary multiobjective optimization—in terms of search efficiency, computational cost, decision making, visualization, and so on. This leads to various research questions, in particular whether certain objectives can be omitted in order to overcome or at least diminish the difficulties that arise when many, that is, more than three, objective functions are involved. This study addresses this question from different perspectives. First, we investigate how adding or omitting objectives affects the problem characteristics an

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32

Dietrich, H. "Applications of Toland's duality theory to nonconvex optimization problems." Optimization 22, no. 6 (1991): 845–54. http://dx.doi.org/10.1080/02331939108843727.

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33

Vannelli, Anthony, Victor H. Quintana, and Luis Vargas. "Interior point optimization methods: Theory, implementations and engineering applications." Canadian Journal of Electrical and Computer Engineering 17, no. 2 (1992): 84–94. http://dx.doi.org/10.1109/cjece.1992.6592637.

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34

Notarstefano, Giuseppe, and Francesco Bullo. "Distributed Abstract Optimization via Constraints Consensus: Theory and Applications." IEEE Transactions on Automatic Control 56, no. 10 (2011): 2247–61. http://dx.doi.org/10.1109/tac.2011.2164020.

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35

Cheng, Ching-Shui. "An Optimization Problem with Applications to Optimal Design Theory." Annals of Statistics 15, no. 2 (1987): 712–23. http://dx.doi.org/10.1214/aos/1176350370.

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36

TEKIN, EYLEM, and IHSAN SABUNCUOGLU. "Simulation optimization: A comprehensive review on theory and applications." IIE Transactions 36, no. 11 (2004): 1067–81. http://dx.doi.org/10.1080/07408170490500654.

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37

Gupta, Pankaj, Riccardo Cambini, and S. S. Appadoo. "Recent advances in optimization theory and applications (RAOTA-2016)." Annals of Operations Research 269, no. 1-2 (2018): 1–2. http://dx.doi.org/10.1007/s10479-018-2984-y.

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38

Audet, Charles, and Michael Kokkolaras. "Blackbox and derivative-free optimization: theory, algorithms and applications." Optimization and Engineering 17, no. 1 (2016): 1–2. http://dx.doi.org/10.1007/s11081-016-9307-4.

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39

Hayashi, Yuzuru, and Rieko Matsuda. "Optimization theory of chromatography." Chemometrics and Intelligent Laboratory Systems 18, no. 1 (1993): 1–16. http://dx.doi.org/10.1016/0169-7439(93)80040-o.

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40

Weber, Gerhard-Wilhelrn. "Generalized semi-infinite optimization : theory and applications in optimal control and discrete optimization." Journal of Statistics and Management Systems 5, no. 1-3 (2002): 359–88. http://dx.doi.org/10.1080/09720510.2002.10701064.

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41

Gao, X. Z., V. Govindasamy, H. Xu, X. Wang, and K. Zenger. "Harmony Search Method: Theory and Applications." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/258491.

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The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem.

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42

Saveliev, Peter. "Applications of Lefschetz Numbers in Control Theory." SIAM Journal on Control and Optimization 44, no. 5 (2005): 1677–90. http://dx.doi.org/10.1137/s0363012904442240.

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43

IOFFE, A. D. "METRIC REGULARITY—A SURVEY PART II. APPLICATIONS." Journal of the Australian Mathematical Society 101, no. 3 (2016): 376–417. http://dx.doi.org/10.1017/s1446788715000695.

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Metric regularity theory lies in the very heart of variational analysis, a relatively new discipline whose appearance was, to a large extent, determined by the needs of modern optimization theory in which such phenomena as nondifferentiability and set-valued mappings naturally appear. The roots of the theory go back to such fundamental results of the classical analysis as the implicit function theorem, Sard theorem and some others. The paper offers a survey of the state of the art of some principal parts of the theory along with a variety of its applications in analysis and optimization.

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44

Bouchard, Bruno, H. Mete Soner, and Nizar Touzi. "Special Issue: Optimization and Stochastic Control in Finance, Journal of Optimization Theory and Applications." Journal of Optimization Theory and Applications 179, no. 2 (2018): 363–65. http://dx.doi.org/10.1007/s10957-018-1409-z.

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45

Liu, Jing, and Huicheng Liu. "Canonical Duality for Box Constrained Nonconvex and Nonsmooth Optimization Problems." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/354263.

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This paper presents an application of the canonical duality theory for box constrained nonconvex and nonsmooth optimization problems. By use of the canonical dual transformation method, which is developed recently, these very difficult constrained optimization problems inRncan be converted into the canonical dual problems, which can be solved by deterministic methods. The global and local extrema can be identified by the triality theory. Some examples are listed to illustrate the applications of the theory presented in the paper.

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46

Kwakernaak, Huibert. "H 2 —Optimization - Theory and Applications to Robust Control Design." IFAC Proceedings Volumes 33, no. 14 (2000): 437–48. http://dx.doi.org/10.1016/s1474-6670(17)36268-7.

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47

Li, Jinlu, Chong Li, Ngai-Ching Wong, and Jen-Chih Yao. "Recent Development in Fixed-Point Theory, Optimization, and Their Applications." Abstract and Applied Analysis 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/293463.

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48

Zhang, Heng, Zhiguo Shi, Mohammed Chadli, Yanzheng Zhu, and Zhaojian Li. "Guest editorial: Networked cyber-physical systems: Optimization theory and applications." Peer-to-Peer Networking and Applications 12, no. 6 (2019): 1624–26. http://dx.doi.org/10.1007/s12083-019-00811-6.

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49

Marec, J. P. "Pierre Contensou (1914–1987) and the optimization theory and applications." Acta Astronautica 32, no. 3 (1994): 239–44. http://dx.doi.org/10.1016/0094-5765(94)90057-4.

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50

Roda, Fabio. "Integrating high-level requirements in optimization problems: theory and applications." 4OR 12, no. 2 (2013): 199–200. http://dx.doi.org/10.1007/s10288-013-0243-2.

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