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Journal articles on the topic 'Optimization-based modeling'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Miftari, Bardhyl, Mathias Berger, Hatim Djelassi, and Damien Ernst. "GBOML: Graph-Based Optimization Modeling Language." Journal of Open Source Software 7, no. 72 (April 22, 2022): 4158. http://dx.doi.org/10.21105/joss.04158.

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2

Windmann, Andreas, Juraj Šimko, and Petra Wagner. "Optimization-based modeling of speech timing." Speech Communication 74 (November 2015): 76–92. http://dx.doi.org/10.1016/j.specom.2015.09.007.

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3

Song, Hui, Eun-Sung Gil, Kwan-Ho Chun, and Sang-Ho Park. "Modeling and Optimization of Active Power Filter Based on a Switched Linear System." Journal of Clean Energy Technologies 5, no. 6 (November 2017): 443–47. http://dx.doi.org/10.18178/jocet.2017.5.6.413.

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4

Tontchev, Nikolay, and Martin Ivanov. "MODELING AND OPTIMIZATION OF THE COMPOSITION OF IRON-BASED ALLOYS BY APPROXIMATION WITH NEURAL MODELS AND GENETIC OPTIMIZATION ALGORITHM." FBIM Transactions 2, no. 1 (January 15, 2014): 1–12. http://dx.doi.org/10.12709/fbim.02.02.01.01.

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5

Queipo, Nestor V., Javier V. Goicochea, and Salvador Pintos. "Surrogate modeling-based optimization of SAGD processes." Journal of Petroleum Science and Engineering 35, no. 1-2 (July 2002): 83–93. http://dx.doi.org/10.1016/s0920-4105(02)00167-5.

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6

Zhao, Dongbin, Yi Shen, Zhanshan Wang, and Xiaolin Hu. "Data-based control, optimization, modeling and applications." Neural Computing and Applications 23, no. 7-8 (January 4, 2013): 1839–42. http://dx.doi.org/10.1007/s00521-012-1319-1.

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7

Yang, Shu, San Kiang, Parham Farzan, and Marianthi Ierapetritou. "Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization." Processes 7, no. 1 (December 29, 2018): 9. http://dx.doi.org/10.3390/pr7010009.

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Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

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8

Abdel-Malek, K., Z. Mi, J. Yang, and K. Nebel. "Optimization-Based Layout Design." Applied Bionics and Biomechanics 2, no. 3-4 (2005): 187–96. http://dx.doi.org/10.1155/2005/285756.

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The layout problem is of importance to ergonomists, vehicle/cockpit packaging engineers, designers of manufacturing assembly lines, designers concerned with the placement of levers, knobs, controls, etc. in the reachable workspace of a human, and also to users of digital human modeling code, where digital prototyping has become a valuable tool. This paper proposes a hybrid optimization method (gradient-based optimization and simulated annealing) to obtain the layout design. We implemented the proposed algorithm for a project at Oral-B Laboratories, where a manufacturing cell involves an operator who handles three objects, some with the left hand, others with the right hand.

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9

Liubogoshchev, Mikhail, Kamila Ragimova, Andrey Lyakhov, Siyu Tang, and Evgeny Khorov. "Adaptive Cloud-Based Extended Reality: Modeling and Optimization." IEEE Access 9 (2021): 35287–99. http://dx.doi.org/10.1109/access.2021.3062555.

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10

Wang, Tianyou, Yongtai Lin, Yinglan Liang, Tao Yang, and Yuhan Li. "Chemical Synthesis Data Modeling Based on Mathematical Optimization." Wireless Communications and Mobile Computing 2022 (June 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/9538852.

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As one of the most important high value-added raw materials in the chemical industry, the synthesis of C4 Olefin by ethanol coupling was of great significance in the field of the chemical industry. Different catalysts and various conditions have different effects on the chemical reaction. This paper is based on the relevant data set. Firstly, Pearson and Spearman correlation coefficient method and corresponding hypothesis test are used to get the influence of different catalysts on the chemical reaction. Ethanol conversion and C4 Olefin selectivity are positively correlated with temperature. Secondly, a multivariate linear regression model with significant core variables is constructed to investigate the effects of catalyst combination and temperature on ethanol conversion and C4 Olefin selectivity. It can be concluded that the ethanol concentration is greatly affected by temperature and CO loading, and there is a positive and negative correlation between ethanol concentration and CO loading. The selectivity of C4 Olefin is affected by temperature and is positively correlated with the charge ratio of CO/SiO2 and HAP. Finally, by using a multiple regression equation and simulated annealing model, it can be obtained that when the loading of CO is 4.75 wt%, the loading ratio of CO/SiO2 and HAP is 1 : 1.4242, the concentration of ethanol is 0.3658 ml/min, and the temperature is 448.21°C, the loading ratio of CO/SiO2 and HAP is 1 : 1.4242, the concentration of ethanol is 0.3658 ml/min, and the temperature is 448.21°C, the yield of C4 Olefin can reach a higher value.

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11

黄, 亚楠. "Parking Lot Optimization Design Based on Mathematical Modeling." Computer Science and Application 12, no. 04 (2022): 1122–38. http://dx.doi.org/10.12677/csa.2022.124115.

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12

黄, 亚楠. "Parking Lot Optimization Design Based on Mathematical Modeling." Computer Science and Application 12, no. 04 (2022): 1122–38. http://dx.doi.org/10.12677/csa.2022.124115.

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13

黄, 亚楠. "Parking Lot Optimization Design Based on Mathematical Modeling." Computer Science and Application 12, no. 04 (2022): 1122–38. http://dx.doi.org/10.12677/csa.2022.124115.

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14

Xu, Yonghao, and Ning Xu. "FPGA autotuning optimization based on structural equation modeling." Procedia Computer Science 183 (2021): 132–38. http://dx.doi.org/10.1016/j.procs.2021.02.041.

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15

Songqing Chen, Bo Shen, S. Wee, and Xiaodong Zhang. "Segment-based streaming media proxy: modeling and optimization." IEEE Transactions on Multimedia 8, no. 2 (April 2006): 243–56. http://dx.doi.org/10.1109/tmm.2005.864281.

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16

Thirugnanasambandham, K., and V. Sivakumar. "Preparation of Chitosan based Nanofibers: Optimization and Modeling." International Journal of Chemical Reactor Engineering 14, no. 1 (February 1, 2016): 283–88. http://dx.doi.org/10.1515/ijcre-2015-0029.

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AbstractThe main objective of the present study is to prepare a chitosan based nanofiber and model the electrospinning process using response surface methodology (RSM). The electrospinning parameters such as collector distance, polymer solution concentration and applied voltage were optimized by using three-variable-three-level Box–Behnken design (BBD). Based on RSM analysis, second order polynomial equation was formed and it indicated good correspondence between experimental and predicted values. 3D response surface plots were used to study the individual and interactive effects of process variables on chitosan based nanofiber diameter. The optimum process conditions for the minimum chitosan based nanofiber diameter (0.3 µm) were found to be collector distance of 12 cm, polymer solution concentration of 25% and applied voltage of 6 kV.

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17

CUI, Qifeng. "Modeling and optimization of MEMS-based piezoelectric micropump." Chinese Journal of Mechanical Engineering 44, no. 12 (2008): 131. http://dx.doi.org/10.3901/jme.2008.12.131.

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18

Häggblom, Kurt E. "DATA-BASED UNCERTAINTY MODELING BY CONVEX OPTIMIZATION TECHNIQUES." IFAC Proceedings Volumes 39, no. 2 (2006): 91–96. http://dx.doi.org/10.3182/20060402-4-br-2902.00091.

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19

Wang, Y., and D. U. Sauer. "Optimization of DMFC regulation based on spatial modeling." International Journal of Hydrogen Energy 40, no. 35 (September 2015): 12023–33. http://dx.doi.org/10.1016/j.ijhydene.2015.06.030.

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20

Liu, Jikai, Qian Chen, Yufan Zheng, Rafiq Ahmad, Jinyuan Tang, and Yongsheng Ma. "Level set-based heterogeneous object modeling and optimization." Computer-Aided Design 110 (May 2019): 50–68. http://dx.doi.org/10.1016/j.cad.2019.01.002.

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21

Chai, Tianyou, Zhongsheng Hou, Frank L. Lewis, Amir Hussain, and Dongbin Zhao. "Guest Editorial Data-Based Control, Modeling, and Optimization." IEEE Transactions on Neural Networks 22, no. 12 (December 2011): 2150–53. http://dx.doi.org/10.1109/tnn.2011.2177733.

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22

Joo, Keehyoung, Jinwoo Lee, Sunjoong Lee, Joo-Hyun Seo, Sung Jong Lee, and Jooyoung Lee. "High accuracy template based modeling by global optimization." Proteins: Structure, Function, and Bioinformatics 69, S8 (2007): 83–89. http://dx.doi.org/10.1002/prot.21628.

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23

Liu, Yong Xia, Ru Shu Peng, Ai Hong Hou, and De Wen Tang. "Methods of Cam Structure Optimization Based on Behavioral Modeling." Advanced Materials Research 139-141 (October 2010): 1245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1245.

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By defining analysis feature and using analysis results to drive the parametric model, behavioral modeling establishes feature parameter of model automatically for meeting design objectives and making model technology intelligent, that is to say the result could be optimized automatically. In this paper, the application of PRO/E parametric modeling technologies in design of cam profile curve is researched. In order to optimization for dynamic balance of the cam, the methods of defining analysis feature and sensitivity/optimization analysis are proposed by using the technology of PRO/E behavioral modeling. These technologies can enhance the efficiency and quality of the cam design and provide a practical method for 3D modeling of this kind of cam. The fifth generation of CAD model technology named behavioral modeling provides the method of flexible and intelligent solution of practical engineering problems.

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24

Jusevičius, Vaidas, and Remigijus Paulavičius. "Web-Based Tool for Algebraic Modeling and Mathematical Optimization." Mathematics 9, no. 21 (October 29, 2021): 2751. http://dx.doi.org/10.3390/math9212751.

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In this article, we present a new open-source tool for algebraic modeling and mathematical optimization. We begin by distilling the main gaps within the existing algebraic modeling languages and tools (varying performance, limited cross-compatibility, complex syntax, and different solver, feature, and problem type support). Later, we propose a state-of-the-art web-based tool (WebAML and Optimization System) for algebraic modeling languages and mathematical optimization. The tool does not require specific algebraic language knowledge, allows solving problems using different solvers, and utilizes the best characteristics of existing algebraic modeling languages. We also provide clear extension points and ideas on how we could further improve such a tool.

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25

Rao, R. Venkata, and Dhiraj P. Rai. "Optimization of fused deposition modeling process using teaching-learning-based optimization algorithm." Engineering Science and Technology, an International Journal 19, no. 1 (March 2016): 587–603. http://dx.doi.org/10.1016/j.jestch.2015.09.008.

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26

Ma, Tieju, and Yoshiteru Nakamori. "Modeling technological change in energy systems – From optimization to agent-based modeling." Energy 34, no. 7 (July 2009): 873–79. http://dx.doi.org/10.1016/j.energy.2009.03.005.

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27

Zuo, Zhi Hao, Mike Xie, and Bang Zhao. "An Innovative Design Based on CAD Environment." Advanced Materials Research 308-310 (August 2011): 1166–69. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1166.

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The mathematical development of structural topology optimization provides a mature tool for design optimization, although the application is still very limited in engineering practice. This paper intends to study the application of topology optimization in industrial design via commercial CAD software interfaces. An innovative numerical procedure for this purpose is introduced based on convenient CAD modeling interfaces. As an instance, the B-spline based environment Rhinoceros3D features the modeling module for the optimization procedure, in collaboration with an additional optimization engine BESO3D. The topology optimization is realized based on the CAD model from Rhinoceros and outputs the optimal solution into Rhinoceros after computation. Further interpretation of the optimization results is discussed within the framework of Rhinoceros. Design applications of the modeling-optimization design integration are showcased as the examples to demonstrate the efficiency and robustness of topology optimization in industrial design.

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28

He, Xue Wen, Gui Xiong Liu, Hai Bing Zhu, and Xiao Ping Zhang. "Method of Optimization for Target Localization Model Parameters Based on LSSVR." Advanced Materials Research 268-270 (July 2011): 934–39. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.934.

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Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

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29

Jalalimanesh, Ammar, Hamidreza Shahabi Haghighi, Abbas Ahmadi, and Madjid Soltani. "Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning." Mathematics and Computers in Simulation 133 (March 2017): 235–48. http://dx.doi.org/10.1016/j.matcom.2016.05.008.

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30

Jae Kyu Lee and Min Yong Kim. "Case-based learning for knowledge-based optimization modeling system: UNIK-CASE." Expert Systems with Applications 6, no. 1 (January 1993): 87–95. http://dx.doi.org/10.1016/0957-4174(93)90021-w.

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31

Feng, Wenying, and Hua Chen. "Performance optimization for web caching based on input modeling." Journal of Computational Methods in Sciences and Engineering 9, s2 (July 30, 2009): S149—S157. http://dx.doi.org/10.3233/jcm-2009-0243.

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32

Lu, Yanglong, and Yan Wang. "Structural optimization of metamaterials based on periodic surface modeling." Computer Methods in Applied Mechanics and Engineering 395 (May 2022): 115057. http://dx.doi.org/10.1016/j.cma.2022.115057.

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33

LIU, Yanbing, Jun HUANG, and Zhangxiong LIU. "Utilization-Based Modeling and Optimization for Cognitive Radio Networks." IEICE Transactions on Communications E92-B, no. 9 (2009): 2976–79. http://dx.doi.org/10.1587/transcom.e92.b.2976.

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34

Marzuoli, Aude, Maxime Gariel, Adan Vela, and Eric Feron. "Data-Based Modeling and Optimization of En Route Traffic." Journal of Guidance, Control, and Dynamics 37, no. 6 (November 2014): 1930–45. http://dx.doi.org/10.2514/1.g000010.

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35

Soilahoudine, Moindze, Christian Gogu, and Christian Bes. "Accelerated Adaptive Surrogate-Based Optimization Through Reduced-Order Modeling." AIAA Journal 55, no. 5 (May 2017): 1681–94. http://dx.doi.org/10.2514/1.j055252.

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36

Yang, Dongzhe, Kourosh Danai, and Sundar Krishnamurty. "Knowledge-Based Interval Modeling Method for Efficient Global Optimization." Journal of Design and Manufacturing Automation 1, no. 1-2 (October 2001): 67–80. http://dx.doi.org/10.1080/15320370108500202.

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37

Yang, Dongzhe, Kourosh Danai, and Sundar Krishnamurty. "Knowledge-Based Interval Modeling Method for Efficient Global Optimization." Journal of Design and Manufacturing Automation 4, no. 1 (October 2001): 67–80. http://dx.doi.org/10.1080/15320370108500214.

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38

Zhao, Bin, Gang Pei, and Aaswath P. Raman. "Modeling and optimization of radiative cooling based thermoelectric generators." Applied Physics Letters 117, no. 16 (October 19, 2020): 163903. http://dx.doi.org/10.1063/5.0022667.

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39

Bambach, Markus, Armin Fügenschuh, Johannes Buhl, Felix Jensch, and Johannes Schmidt. "Mathematical Modeling and Optimization for Powder-Based Additive Manufacturing." Procedia Manufacturing 47 (2020): 1159–63. http://dx.doi.org/10.1016/j.promfg.2020.04.158.

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40

Tahmasebi, Pejman. "An optimization-based approach for modeling of complex particles." Powder Technology 356 (November 2019): 342–52. http://dx.doi.org/10.1016/j.powtec.2019.08.027.

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41

Rahnama, Amir Hossein Akhavan, Mehdi Toloo, and Nezer Jacob Zaidenberg. "An LP-based hyperparameter optimization model for language modeling." Journal of Supercomputing 74, no. 5 (January 9, 2018): 2151–60. http://dx.doi.org/10.1007/s11227-018-2236-6.

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42

Benson, J. "Cell based modeling and optimization of ovarian follicle cryopreservation." Cryobiology 73, no. 3 (December 2016): 416. http://dx.doi.org/10.1016/j.cryobiol.2016.09.069.

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43

Dong, Ziqian, Rohan D. W. Perera, Rajarathnam Chandramouli, and K. P. Subbalakshmi. "Network measurement based modeling and optimization for IP geolocation." Computer Networks 56, no. 1 (January 2012): 85–98. http://dx.doi.org/10.1016/j.comnet.2011.08.011.

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44

Liu, Sha, Xianhai Meng, and Chiming Tam. "Building information modeling based building design optimization for sustainability." Energy and Buildings 105 (October 2015): 139–53. http://dx.doi.org/10.1016/j.enbuild.2015.06.037.

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45

Yang, Jian, and Xuejun Huang. "Intelligent Planning Modeling and Optimization of UAV Cluster Based on Multi-Objective Optimization Algorithm." Electronics 11, no. 24 (December 19, 2022): 4238. http://dx.doi.org/10.3390/electronics11244238.

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As a flight tool integrating carrier and reconnaissance, unmanned aerial vehicles (UAVs) are applied in various fields. In recent years, mission planning and path optimization have become the most important research focuses in the field of UAVs. With the continuous maturity of artificial intelligence technology, various search algorithms have been applied in the field of unmanned aerial vehicles. However, these algorithms have certain defects, which lead to problems, such as large search volume and low efficiency in task planning, and cannot meet the requirements of path planning. The objective optimization algorithm has a good performance in solving optimization problems. In this paper, the intelligent planning model of UAV cluster was established based on multi-objective optimization algorithm, and its path is optimized. In the aspect of modeling, this paper studied and analyzed online task planning, search rules and cluster formation control using an agent-based intelligent modeling method. For mission planning and optimization, it combined multi-objective optimization algorithm to build the model from three aspects of mission allocation, route planning and planning evaluation. The final simulation results showed that the UAV cluster intelligent planning modeling method and path optimization method based on multi-objective optimization algorithm met the requirements of route design and improved the path search efficiency with 2.26% task completion satisfaction.

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46

Lu, Chenyang, Su Deng, Yahui Wu, Haohao Zhou, and Wubin Ma. "Federated Learning Based on OPTICS Clustering Optimization." Discrete Dynamics in Nature and Society 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/7151373.

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Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. In the real environment, the distribution of data among nodes is often inconsistent. By analyzing the influence of independent identically distributed data (non-IID) on the accuracy of FL, it is shown that the accuracy of the model obtained by the traditional FL method is low. Therefore, we proposed the diversified sampling strategies to simulate the non-IID data situation and came up with the OPTICS (ordering points to identify the clustering structure)-based clustering optimization federated learning method (OCFL), which solves the problem that the learning accuracy is reduced when the data of different nodes are non-IID in FL. Experiments indicate that OCFL greatly improves the model accuracy and training speed compared with the traditional FL algorithm.

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47

Shi, Zeyun, Jinkeng Lin, Jiong Chen, Yao Jin, and Jin Huang. "Symmetry Based Material Optimization." Symmetry 13, no. 2 (February 14, 2021): 315. http://dx.doi.org/10.3390/sym13020315.

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Many man-made or natural objects are composed of symmetric parts and possess symmetric physical behavior. Although its shape can exactly follow a symmetry in the designing or modeling stage, its discretized mesh in the analysis stage may be asymmetric because generating a mesh exactly following the symmetry is usually costly. As a consequence, the expected symmetric physical behavior may not be faithfully reproduced due to the asymmetry of the mesh. To solve this problem, we propose to optimize the material parameters of the mesh for static and kinematic symmetry behavior. Specifically, under the situation of static equilibrium, Young’s modulus is properly scaled so that a symmetric force field leads to symmetric displacement. For kinematics, the mass is optimized to reproduce symmetric acceleration under a symmetric force field. To efficiently measure the deviation from symmetry, we formulate a linear operator whose kernel contains all the symmetric vector fields, which helps to characterize the asymmetry error via a simple ℓ2 norm. To make the resulting material suitable for the general situation, the symmetric training force fields are derived from modal analysis in the above kernel space. Results show that our optimized material significantly reduces the asymmetric error on an asymmetric mesh in both static and dynamic simulations.

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48

Ming, Zhe. "The Modeling Gear Optimization Design Research Based on Pro-E." Applied Mechanics and Materials 411-414 (September 2013): 452–57. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.452.

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With the development of computer technology, gear modeling in industry more and more widely used, the PRO-E tool has good modeling function, on the design of three-dimensional CAD tools has a significant role, the tracing point method, parameter method, plug-in method analysis, optimum gear profile modeling. On the tooth height model modification method, draw gear high maximum model modification, model modification can be assigned in the active or passive gear wheel tooth, but also at the same time in the driving wheel and a driven wheel model modification. Corresponding to a single tooth meshing zone involute not model modification, only to repair the dual meshing zone, reach the gear model optimization objective.

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49

Ding, Xiaobing, Zhigang Liu, Gan Shi, Hua Hu, Jiaping Chen, Kaihe Yang, Su Wan, and Jinlong Wu. "The Optimization of Airport Management Based on Collaborative Optimization of Flights and Taxis." Discrete Dynamics in Nature and Society 2022 (February 4, 2022): 1–16. http://dx.doi.org/10.1155/2022/4542299.

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How to efficiently guide passengers and ensure the order of airport operation is an urgent transport problem for airport management. Based on the analysis of the factors that affect the driver’s decision-making, this paper deeply explores the collaborative association of the core factors, such as the number of flight arrivals in different periods and the average seeking distance of taxis. Firstly, according to the GPS data of taxis, the paper uses clustering algorithm to get the average passenger-seeking time from the airport and makes matching interaction between the number of flights based on time distribution and the average passenger-carrying capacity of vehicles in the parking garage, so as to build a decision-making model based on the number of taxis N; secondly, it takes passenger safety and traffic order as the priority and uses M/M/S queuing model to integrate the two factors. Taking the maintenance cost and passenger evacuation time as constraints, the judgment condition of minimum cost Zmin and the optimal number of boarding points Sm are solved. Finally, taking the flight and taxi data of Shanghai Hongqiao Airport as an example, the driver’s decision-making standard is simulated, and the accuracy of the model is verified by the deviation rate. It can provide decision-making support for taxi management of urban transportation hub and rapid evacuation of airport passengers, so as to realize the collaborative optimization of airport flight arrival and taxi carrying order.

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50

He, Xue Wen, and Wen Qian Huang. "Study on LSSVR Modeling Positioning Based on Particle Swarm Optimization." Advanced Materials Research 791-793 (September 2013): 1096–99. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1096.

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Particle Swarm Optimization (PSO) is a new intelligent optimization algorithm and it iteratively searches the parameter space in parallel to optimize. The method is simple and practical, with fewer adjustable parameters which has been widely researched and applied. This paper intends to make PSO to the modeling parameter of the least squares support vector machine (LSSVM), to improve its positioning effect and precision of modeling positioning.

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