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Gotowe bibliografie tematyczne / Global Optimization / Artykuły w czasopismach

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Artykuły w czasopismach na temat „Global Optimization”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Sahinidis, Nikolaos V. "Global optimization." Optimization Methods and Software 24, no. 4-5 (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, no. 6 (2011): 879–87. http://dx.doi.org/10.1002/wcms.70.

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3

Stephens, C. P., and W. Baritompa. "Global Optimization Requires Global Information." Journal of Optimization Theory and Applications 96, no. 3 (1998): 575–88. http://dx.doi.org/10.1023/a:1022612511618.

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4

Cui-Cui Cai, Cui-Cui Cai, Mao-Sheng Fu Cui-Cui Cai, Xian-Meng Meng Mao-Sheng Fu, Qi-Jian Wang Xian-Meng Meng, and Yue-Qin Wang Qi-Jian Wang. "Modified Harris Hawks Optimization Algorithm with Multi-strategy for Global Optimization Problem." 電腦學刊 34, no. 6 (2023): 091–105. http://dx.doi.org/10.53106/199115992023123406007.

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<p>As a novel metaheuristic algorithm, the Harris Hawks Optimization (HHO) algorithm has excellent search capability. Similar to other metaheuristic algorithms, the HHO algorithm has low convergence accuracy and easily traps in local optimal when dealing with complex optimization problems. A modified Harris Hawks optimization (MHHO) algorithm with multiple strategies is presented to overcome this defect. First, chaotic mapping is used for population initialization to select an appropriate initiation position. Then, a novel nonlinear escape energy update strategy is presented to control t

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5

Black, Fischer, and Robert Litterman. "Global Portfolio Optimization." Financial Analysts Journal 48, no. 5 (1992): 28–43. http://dx.doi.org/10.2469/faj.v48.n5.28.

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6

Sellis, Timos K. "Global query optimization." ACM SIGMOD Record 15, no. 2 (1986): 191–205. http://dx.doi.org/10.1145/16856.16874.

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7

Odili, Julius Beneoluchi, and A. Noraziah. "African Buffalo Optimization for Global Optimization." Current Science 114, no. 03 (2018): 627. http://dx.doi.org/10.18520/cs/v114/i03/627-636.

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8

Ng, Chi-Kong, Duan Li, and Lian-Sheng Zhang. "Global Descent Method for Global Optimization." SIAM Journal on Optimization 20, no. 6 (2010): 3161–84. http://dx.doi.org/10.1137/090749815.

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9

Beheshti, Zahra, Siti Mariyam Shamsuddin, and 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 multimo

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10

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

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11

Kassoul, Khelil, Nicolas Zufferey, Naoufel Cheikhrouhou, and 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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12

Wen, Heng, Su Xin Wang, Fu Qiang Lu, et al. "Colony search optimization algorithm using global optimization." Journal of Supercomputing 78, no. 5 (2021): 6567–611. http://dx.doi.org/10.1007/s11227-021-04127-2.

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13

Ismaeel, Alaa A. K., Islam A. Elshaarawy, Essam H. Houssein, Fatma Helmy Ismail, and 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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14

Tuba, Milan, Ivona Brajevic, and Raka Jovanovic. "Hybrid Seeker Optimization Algorithm for Global Optimization." Applied Mathematics & Information Sciences 7, no. 3 (2013): 867–75. http://dx.doi.org/10.12785/amis/070304.

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15

Arora, Sankalap, and Priyanka Anand. "Chaotic grasshopper optimization algorithm for global optimization." Neural Computing and Applications 31, no. 8 (2018): 4385–405. http://dx.doi.org/10.1007/s00521-018-3343-2.

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16

Wu, Z. Y., D. Li, and L. S. Zhang. "Global descent methods for unconstrained global optimization." Journal of Global Optimization 50, no. 3 (2010): 379–96. http://dx.doi.org/10.1007/s10898-010-9587-8.

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17

WU, ZHIYOU, FUSHENG BAI, and LIANSHENG ZHANG. "MONOTONIZATION IN GLOBAL OPTIMIZATION." Chinese Annals of Mathematics 26, no. 03 (2005): 475–90. http://dx.doi.org/10.1142/s0252959905000385.

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18

Tofallis, Chris, Reiner Horst, Panos M. Pardalos, and Nguyen V. Thoai. "Introduction to Global Optimization." Journal of the Operational Research Society 47, no. 10 (1996): 1314. http://dx.doi.org/10.2307/3010048.

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19

Tofallis, Chris, Reiner Horst, Panos M. Padalos, and Nguyen V. Thoai. "Introduction to Global Optimization." Journal of the Operational Research Society 47, no. 7 (1996): 962. http://dx.doi.org/10.2307/3010143.

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20

Mardle, S. J., I. M. Bomze, T. Csendes, R. Horst, and P. M. Pardalos. "Developments in Global Optimization." Journal of the Operational Research Society 50, no. 3 (1999): 286. http://dx.doi.org/10.2307/3010697.

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21

Kafandaris, S., R. Horst, and H. Tuy. "Global Optimization (3rd Edition)." Journal of the Operational Research Society 48, no. 8 (1997): 850. http://dx.doi.org/10.2307/3010719.

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22

Bilbro, G. L. "Fast stochastic global optimization." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 4 (1994): 684–89. http://dx.doi.org/10.1109/21.286389.

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23

Geman, Stuart, and Chii-Ruey Hwang. "Diffusions for Global Optimization." SIAM Journal on Control and Optimization 24, no. 5 (1986): 1031–43. http://dx.doi.org/10.1137/0324060.

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24

Tofallis, Chris. "Introduction to Global Optimization." Journal of the Operational Research Society 47, no. 7 (1996): 962. http://dx.doi.org/10.1057/jors.1996.123.

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25

Tofallis, Chris. "Introduction to Global Optimization." Journal of the Operational Research Society 47, no. 10 (1996): 1314–15. http://dx.doi.org/10.1057/jors.1996.163.

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26

Jagadish, H. V., Hui Jin, Beng Chin Ooi, and Kian-Lee Tan. "Global optimization of histograms." ACM SIGMOD Record 30, no. 2 (2001): 223–34. http://dx.doi.org/10.1145/376284.375687.

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27

Horst, R., and H. Tuy. "Global Optimization (3rd edition)." Journal of the Operational Research Society 48, no. 8 (1997): 850. http://dx.doi.org/10.1057/palgrave.jors.2600835.

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28

Fang, Hai-Tao, and Han-Fu Chen. "Global recursive stochastic optimization." IFAC Proceedings Volumes 32, no. 2 (1999): 5029–34. http://dx.doi.org/10.1016/s1474-6670(17)56856-1.

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29

Doye, Jonathan P. K., and David J. Wales. "Thermodynamics of Global Optimization." Physical Review Letters 80, no. 7 (1998): 1357–60. http://dx.doi.org/10.1103/physrevlett.80.1357.

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30

Mora, G., Y. Cherruault, and A. Benabidallah. "Global optimization‐preserving operators." Kybernetes 32, no. 9/10 (2003): 1473–80. http://dx.doi.org/10.1108/03684920310493369.

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31

Horst, R., and H. Tuy. "Global Optimization (3rd edition)." Journal of the Operational Research Society 48, no. 8 (1997): 850. http://dx.doi.org/10.1038/sj.jors.2600835.

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32

Aloise, Daniel, Pierre Hansen, and Caroline Rocha. "Global optimization workshop 2012." Journal of Global Optimization 60, no. 2 (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, no. 2 (1991): 131–44. http://dx.doi.org/10.1007/bf00119987.

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34

Galperin, E. A. "Nonscalarized multiobjective global optimization." Journal of Optimization Theory and Applications 75, no. 1 (1992): 69–85. http://dx.doi.org/10.1007/bf00939906.

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35

Yi-Lin, Cao, and Wang Yan-Song. "Global Geometry Optimization of Water Clusters Using Fast Hybrid Global Optimization Algorithm." Acta Physico-Chimica Sinica 20, no. 08 (2004): 785–89. http://dx.doi.org/10.3866/pku.whxb20040801.

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36

Deschaine, Larry M. "Book Review: Global Optimization Toolbox for Maple and Global Optimization with Maple." International Journal of Modelling, Identification and Control 1, no. 4 (2006): 338. http://dx.doi.org/10.1504/ijmic.2006.012709.

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37

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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38

Turdiqulovich, Jonibek Usmonov. "Optimization of Global Information Flows in Transport System Management." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 2024–32. http://dx.doi.org/10.5373/jardcs/v12sp7/20202319.

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39

Wang, Wei, Xiaoshan Zhang, and Min Li. "A Filled Function Method Dominated by Filter for Nonlinearly Global Optimization." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/245427.

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This work presents a filled function method based on the filter technique for global optimization. Filled function method is one of the effective methods for nonlinear global optimization, since it can effectively find a better minimizer. Filter technique is applied to local optimization methods for its excellent numerical results. In order to optimize the filled function method, the filter method is employed for global optimizations in this method. A new filled function is proposed first, and then the algorithm and its properties are proved. The numerical results are listed at the end.

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40

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

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41

ChengHong Yang, ShengWei Tsai, LiYeh Chuang, and ChengHuei Yang. "A Modified Particle Swarm Optimization for Global Optimization." International Journal of Advancements in Computing Technology 3, no. 7 (2011): 169–89. http://dx.doi.org/10.4156/ijact.vol3.issue7.22.

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42

Yan, YAN, MA Hongzhong, and LI Zhendong. "An Improved Grasshopper Optimization Algorithm for Global Optimization." Chinese Journal of Electronics 30, no. 3 (2021): 451–59. http://dx.doi.org/10.1049/cje.2021.03.008.

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43

Lee, Chun-Yao, and Guang-Lin Zhuo. "A Hybrid Whale Optimization Algorithm for Global Optimization." Mathematics 9, no. 13 (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 t

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44

Joshi, Himani, and Sankalap Arora. "Enhanced Grey Wolf Optimization Algorithm for Global Optimization." Fundamenta Informaticae 153, no. 3 (2017): 235–64. http://dx.doi.org/10.3233/fi-2017-1539.

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45

Zhang, Qian, Huiling Chen, Jie Luo, Yueting Xu, Chengwen Wu, and 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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46

Arora, Sankalap, and Satvir Singh. "An Improved Butterfly Optimization Algorithm for Global Optimization." Advanced Science, Engineering and Medicine 8, no. 9 (2016): 711–17. http://dx.doi.org/10.1166/asem.2016.1904.

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47

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

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48

L. Mason, T., C. Emelle, J. van Berkel, A. M. Bagirov, F. Kampas, and J. D. Pintér. "Integrated production system optimization using global optimization techniques." Journal of Industrial & Management Optimization 3, no. 2 (2007): 257–77. http://dx.doi.org/10.3934/jimo.2007.3.257.

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49

Akyol, Sinem, Muhammed Yildirim, and Bilal Alatas. "CIDO: Chaotically Initialized Dandelion Optimization for Global Optimization." International Journal of Advanced Networking and Applications 14, no. 06 (2023): 5696–704. http://dx.doi.org/10.35444/ijana.2023.14606.

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Metaheuristic algorithms are widely used for problems in many fields such as security, health, engineering. No metaheuristic algorithm can achieve the optimum solution for all optimization problems. For this, new metaheuristic methods are constantly being proposed and existing ones are being developed. Dandelion Optimizer, one of the most recent metaheuristic algorithms, is biology-based. Inspired by the wind-dependent long-distance flight of the ripening seed of the dandelion plant. It consists of three phases: ascending phase, descending phase and landing phase. In this study, the chaos-base

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50

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

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