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Artykuły w czasopismach na temat „Global Optimization”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 10 marca 2023

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1

Sahinidis, Nikolaos V. "Global optimization". Optimization Methods and Software 24, nr 4-5 (październik 2009): 479–82. http://dx.doi.org/10.1080/10556780903135287.

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2

Hartke, Bernd. "Global optimization". Wiley Interdisciplinary Reviews: Computational Molecular Science 1, nr 6 (12.05.2011): 879–87. http://dx.doi.org/10.1002/wcms.70.

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3

Stephens, C. P., i W. Baritompa. "Global Optimization Requires Global Information". Journal of Optimization Theory and Applications 96, nr 3 (marzec 1998): 575–88. http://dx.doi.org/10.1023/a:1022612511618.

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4

Black, Fischer, i Robert Litterman. "Global Portfolio Optimization". Financial Analysts Journal 48, nr 5 (wrzesień 1992): 28–43. http://dx.doi.org/10.2469/faj.v48.n5.28.

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5

Sellis, Timos K. "Global query optimization". ACM SIGMOD Record 15, nr 2 (15.06.1986): 191–205. http://dx.doi.org/10.1145/16856.16874.

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6

Odili, Julius Beneoluchi, i A. Noraziah. "African Buffalo Optimization for Global Optimization". Current Science 114, nr 03 (10.02.2018): 627. http://dx.doi.org/10.18520/cs/v114/i03/627-636.

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7

Ng, Chi-Kong, Duan Li i Lian-Sheng Zhang. "Global Descent Method for Global Optimization". SIAM Journal on Optimization 20, nr 6 (styczeń 2010): 3161–84. http://dx.doi.org/10.1137/090749815.

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8

Beheshti, Zahra, Siti Mariyam Shamsuddin i Sarina Sulaiman. "Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems". Mathematical Problems in Engineering 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/907386.

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In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.

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9

Ouyang, Hai-bin, Li-qun Gao, Xiang-yong Kong, De-xuan Zou i Steven Li. "Teaching-learning based optimization with global crossover for global optimization problems". Applied Mathematics and Computation 265 (sierpień 2015): 533–56. http://dx.doi.org/10.1016/j.amc.2015.05.012.

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10

Kassoul, Khelil, Nicolas Zufferey, Naoufel Cheikhrouhou i Samir Brahim Belhaouari. "Exponential Particle Swarm Optimization for Global Optimization". IEEE Access 10 (2022): 78320–44. http://dx.doi.org/10.1109/access.2022.3193396.

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11

Wen, Heng, Su Xin Wang, Fu Qiang Lu, Ming Feng, Lei Zhen Wang, Jun Kai Xiong i Ma Cong Si. "Colony search optimization algorithm using global optimization". Journal of Supercomputing 78, nr 5 (22.10.2021): 6567–611. http://dx.doi.org/10.1007/s11227-021-04127-2.

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12

Ismaeel, Alaa A. K., Islam A. Elshaarawy, Essam H. Houssein, Fatma Helmy Ismail i Aboul Ella Hassanien. "Enhanced Elephant Herding Optimization for Global Optimization". IEEE Access 7 (2019): 34738–52. http://dx.doi.org/10.1109/access.2019.2904679.

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13

Tuba, Milan, Ivona Brajevic i Raka Jovanovic. "Hybrid Seeker Optimization Algorithm for Global Optimization". Applied Mathematics & Information Sciences 7, nr 3 (1.05.2013): 867–75. http://dx.doi.org/10.12785/amis/070304.

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14

Arora, Sankalap, i Priyanka Anand. "Chaotic grasshopper optimization algorithm for global optimization". Neural Computing and Applications 31, nr 8 (29.01.2018): 4385–405. http://dx.doi.org/10.1007/s00521-018-3343-2.

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15

Wu, Z. Y., D. Li i L. S. Zhang. "Global descent methods for unconstrained global optimization". Journal of Global Optimization 50, nr 3 (28.07.2010): 379–96. http://dx.doi.org/10.1007/s10898-010-9587-8.

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16

WU, ZHIYOU, FUSHENG BAI i LIANSHENG ZHANG. "MONOTONIZATION IN GLOBAL OPTIMIZATION". Chinese Annals of Mathematics 26, nr 03 (lipiec 2005): 475–90. http://dx.doi.org/10.1142/s0252959905000385.

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17

Tofallis, Chris, Reiner Horst, Panos M. Pardalos i Nguyen V. Thoai. "Introduction to Global Optimization." Journal of the Operational Research Society 47, nr 10 (październik 1996): 1314. http://dx.doi.org/10.2307/3010048.

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18

Tofallis, Chris, Reiner Horst, Panos M. Padalos i Nguyen V. Thoai. "Introduction to Global Optimization." Journal of the Operational Research Society 47, nr 7 (lipiec 1996): 962. http://dx.doi.org/10.2307/3010143.

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19

Mardle, S. J., I. M. Bomze, T. Csendes, R. Horst i P. M. Pardalos. "Developments in Global Optimization." Journal of the Operational Research Society 50, nr 3 (marzec 1999): 286. http://dx.doi.org/10.2307/3010697.

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20

Kafandaris, S., R. Horst i H. Tuy. "Global Optimization (3rd Edition)." Journal of the Operational Research Society 48, nr 8 (sierpień 1997): 850. http://dx.doi.org/10.2307/3010719.

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21

Bilbro, G. L. "Fast stochastic global optimization". IEEE Transactions on Systems, Man, and Cybernetics 24, nr 4 (kwiecień 1994): 684–89. http://dx.doi.org/10.1109/21.286389.

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22

Geman, Stuart, i Chii-Ruey Hwang. "Diffusions for Global Optimization". SIAM Journal on Control and Optimization 24, nr 5 (lipiec 1986): 1031–43. http://dx.doi.org/10.1137/0324060.

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23

Tofallis, Chris. "Introduction to Global Optimization". Journal of the Operational Research Society 47, nr 7 (lipiec 1996): 962. http://dx.doi.org/10.1057/jors.1996.123.

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24

Tofallis, Chris. "Introduction to Global Optimization". Journal of the Operational Research Society 47, nr 10 (październik 1996): 1314–15. http://dx.doi.org/10.1057/jors.1996.163.

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25

Jagadish, H. V., Hui Jin, Beng Chin Ooi i Kian-Lee Tan. "Global optimization of histograms". ACM SIGMOD Record 30, nr 2 (czerwiec 2001): 223–34. http://dx.doi.org/10.1145/376284.375687.

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26

Horst, R., i H. Tuy. "Global Optimization (3rd edition)". Journal of the Operational Research Society 48, nr 8 (sierpień 1997): 850. http://dx.doi.org/10.1057/palgrave.jors.2600835.

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27

Fang, Hai-Tao, i Han-Fu Chen. "Global recursive stochastic optimization". IFAC Proceedings Volumes 32, nr 2 (lipiec 1999): 5029–34. http://dx.doi.org/10.1016/s1474-6670(17)56856-1.

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28

Doye, Jonathan P. K., i David J. Wales. "Thermodynamics of Global Optimization". Physical Review Letters 80, nr 7 (16.02.1998): 1357–60. http://dx.doi.org/10.1103/physrevlett.80.1357.

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29

Mora, G., Y. Cherruault i A. Benabidallah. "Global optimization‐preserving operators". Kybernetes 32, nr 9/10 (grudzień 2003): 1473–80. http://dx.doi.org/10.1108/03684920310493369.

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30

Horst, R., i H. Tuy. "Global Optimization (3rd edition)". Journal of the Operational Research Society 48, nr 8 (1997): 850. http://dx.doi.org/10.1038/sj.jors.2600835.

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31

Galperin, E. A. "Nonscalarized multiobjective global optimization". Journal of Optimization Theory and Applications 75, nr 1 (październik 1992): 69–85. http://dx.doi.org/10.1007/bf00939906.

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32

Aloise, Daniel, Pierre Hansen i Caroline Rocha. "Global optimization workshop 2012". Journal of Global Optimization 60, nr 2 (24.07.2014): 121–22. http://dx.doi.org/10.1007/s10898-014-0219-6.

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33

Tind, J�rgen. "Decomposition in global optimization". Journal of Global Optimization 1, nr 2 (1991): 131–44. http://dx.doi.org/10.1007/bf00119987.

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34

KAWAMOTO, Atsushi. "W122001 Do we need really global optimization for topology optimization?" Proceedings of Mechanical Engineering Congress, Japan 2015 (2015): _W122001–1—_W122001–4. http://dx.doi.org/10.1299/jsmemecj.2015._w122001-1.

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35

Turdiqulovich, Jonibek Usmonov. "Optimization of Global Information Flows in Transport System Management". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25.07.2020): 2024–32. http://dx.doi.org/10.5373/jardcs/v12sp7/20202319.

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36

Yi-Lin, Cao, i Wang Yan-Song. "Global Geometry Optimization of Water Clusters Using Fast Hybrid Global Optimization Algorithm". Acta Physico-Chimica Sinica 20, nr 08 (2004): 785–89. http://dx.doi.org/10.3866/pku.whxb20040801.

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37

Deschaine, Larry M. "Book Review: Global Optimization Toolbox for Maple and Global Optimization with Maple". International Journal of Modelling, Identification and Control 1, nr 4 (2006): 338. http://dx.doi.org/10.1504/ijmic.2006.012709.

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38

Rajakumar, R., Kaushik Sekaran, Ching-Hsien Hsu i Seifedine Kadry. "Accelerated grey wolf optimization for global optimization problems". Technological Forecasting and Social Change 169 (sierpień 2021): 120824. http://dx.doi.org/10.1016/j.techfore.2021.120824.

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39

ChengHong Yang, ShengWei Tsai, LiYeh Chuang i ChengHuei Yang. "A Modified Particle Swarm Optimization for Global Optimization". International Journal of Advancements in Computing Technology 3, nr 7 (31.08.2011): 169–89. http://dx.doi.org/10.4156/ijact.vol3.issue7.22.

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40

Yan, YAN, MA Hongzhong i LI Zhendong. "An Improved Grasshopper Optimization Algorithm for Global Optimization". Chinese Journal of Electronics 30, nr 3 (maj 2021): 451–59. http://dx.doi.org/10.1049/cje.2021.03.008.

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41

Lee, Chun-Yao, i Guang-Lin Zhuo. "A Hybrid Whale Optimization Algorithm for Global Optimization". Mathematics 9, nr 13 (24.06.2021): 1477. http://dx.doi.org/10.3390/math9131477.

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This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

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42

Joshi, Himani, i Sankalap Arora. "Enhanced Grey Wolf Optimization Algorithm for Global Optimization". Fundamenta Informaticae 153, nr 3 (16.06.2017): 235–64. http://dx.doi.org/10.3233/fi-2017-1539.

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43

Zhang, Qian, Huiling Chen, Jie Luo, Yueting Xu, Chengwen Wu i Chengye Li. "Chaos Enhanced Bacterial Foraging Optimization for Global Optimization". IEEE Access 6 (2018): 64905–19. http://dx.doi.org/10.1109/access.2018.2876996.

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44

Arora, Sankalap, i Satvir Singh. "An Improved Butterfly Optimization Algorithm for Global Optimization". Advanced Science, Engineering and Medicine 8, nr 9 (1.09.2016): 711–17. http://dx.doi.org/10.1166/asem.2016.1904.

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45

Beheshti, Zahra, i Siti Mariyam Shamsuddin. "Non-parametric particle swarm optimization for global optimization". Applied Soft Computing 28 (marzec 2015): 345–59. http://dx.doi.org/10.1016/j.asoc.2014.12.015.

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46

L. Mason, T., C. Emelle, J. van Berkel, A. M. Bagirov, F. Kampas i J. D. Pintér. "Integrated production system optimization using global optimization techniques". Journal of Industrial & Management Optimization 3, nr 2 (2007): 257–77. http://dx.doi.org/10.3934/jimo.2007.3.257.

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47

Ketfi-cherif, Amine, i Abdelkader Ziadi. "Global descent method for constrained continuous global optimization". Applied Mathematics and Computation 244 (październik 2014): 209–21. http://dx.doi.org/10.1016/j.amc.2014.06.089.

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48

Wang, Long, Theodore T. Allen i Michael A. Groeber. "Tabu efficient global optimization with applications in additive manufacturing". Structural and Multidisciplinary Optimization 63, nr 6 (26.02.2021): 2811–33. http://dx.doi.org/10.1007/s00158-021-02843-0.

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AbstractMethods based on Gaussian stochastic process (GSP) models and expected improvement (EI) functions have been promising for box-constrained expensive optimization problems. These include robust design problems with environmental variables having set-type constraints. However, the methods that combine GSP and EI sub-optimizations suffer from the following problem, which limits their computational performance. Efficient global optimization (EGO) methods often repeat the same or nearly the same experimental points. We present a novel EGO-type constraint-handling method that maintains a so-called tabu list to avoid past points. Our method includes two types of penalties for the key “infill” optimization, which selects the next test runs. We benchmark our tabu EGO algorithm with five alternative approaches, including DIRECT methods using nine test problems and two engineering examples. The engineering examples are based on additive manufacturing process parameter optimization informed using point-based thermal simulations and robust-type quality constraints. Our test problems span unconstrained, simply constrained, and robust constrained problems. The comparative results imply that tabu EGO offers very promising computational performance for all types of black-box optimization in terms of convergence speed and the quality of the final solution.

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49

Hunjan, Jagtar, i R. Ramaswamy. "Global Optimization by Adiabatic Switching". International Journal of Molecular Sciences 3, nr 1 (31.01.2002): 30–37. http://dx.doi.org/10.3390/i3010030.

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50

Wood, Graham R., Christodoulos A. Floudas i Panos M. Pardalos. "Recent Advances in Global Optimization." Mathematics of Computation 60, nr 202 (kwiecień 1993): 870. http://dx.doi.org/10.2307/2153139.

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