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Journal articles on the topic 'Optimization parameters'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Zhang, Qiantu, Liqing Fang, Leilei Ma, and Yulong Zhao. "Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm." International Journal of Computer Theory and Engineering 8, no. 6 (2016): 500–505. http://dx.doi.org/10.7763/ijcte.2016.v8.1096.

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2

Khare, Manu, and Deepak Kumar Deepak Kumar. "Optimization of Sand Casting Parameters using Factorial Design." International Journal of Scientific Research 3, no. 1 (2012): 151–53. http://dx.doi.org/10.15373/22778179/jan2014/49.

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3

Marko, Hrelja, Klancnik Simon, Irgolic Tomaz, Paulic Matej, Balic Joze, and Brezocnik Miran. "Turning Parameters Optimization Using Particle Swarm Optimization." Procedia Engineering 69 (2014): 670–77. http://dx.doi.org/10.1016/j.proeng.2014.03.041.

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4

Дьяков, Иван, and Ivan D'yakov. "OPTIMIZATION OF TIRE PARAMETERS." Bulletin of Bryansk state technical university 2018, no. 9 (2018): 33–38. http://dx.doi.org/10.30987/article_5bd17b4722c1f0.54739329.

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5

Aly, Nael A. "Optimization of testability parameters." Computers & Industrial Engineering 17, no. 1-4 (1989): 333–39. http://dx.doi.org/10.1016/0360-8352(89)90084-3.

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6

Rajeanderan Revichandran, Jaffar Syed Mohamed Ali, Moumen Idres, and A. K. M. Mohiuddin. "A Review of HVAC System Optimization and Its Effects on Saving Total Energy Utilization of a Building." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, no. 1 (2022): 64–82. http://dx.doi.org/10.37934/arfmts.93.1.6482.

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The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality

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7

Abd El-Hamid, Ahmed S., Ahmed H. Eissa, and ALy M. Radwan. "Levenberg – Marquardt’s Algorithm used for PID Controller Parameters Optimization." International Journal of Engineering Research 4, no. 6 (2015): 286–90. http://dx.doi.org/10.17950/ijer/v4s6/603.

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8

S, Rajesh A., and Dr M. S. Prabuswamy. "An Overview of PSO in Optimization of Machining Parameters." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 150–57. http://dx.doi.org/10.31142/ijtsrd8383.

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9

Minglin, Chen, Lv Enli, and Gan Ling. "Experimental Optimization Of Litchi Spray Pre-Cooling Process Parameters." American Journal of Engineering And Techonology 02, no. 08 (2020): 45–52. http://dx.doi.org/10.37547/tajet/volume02issue08-06.

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10

Kumar, Ramesh, Ashwani Kumar, Laxmikant Laxmikant, Nitesh Dutt, and Varun Pratap Singh. "Cutting Parameters Optimization in Turning Operation using Taguschi Method." International Journal of Materials Manufacturing and Sustainable Technologies 1, no. 2 (2022): 22–30. http://dx.doi.org/10.56896/ijmmst.2022.1.2.009.

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The objective of this study is to optimise the cutting parameters in Turning Operation. For this objective Taguchi Method was used. S/N ratio, ANNOVA was used to investigate the cutting force components & Material removal rate (MRR). In results optimal cutting parameters were obtained and the factors affecting cutting performance were analysed. It was concluded that cutting parameters i.e. depth of cut has influence on cutting force and material removal rate.

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11

Yusupov, A. K., H. M. Musеlеmov, and T. O. Ustarhanov. "Optimization of strutted beam parameters." Herald of Dagestan State Technical University. Technical Sciences 47, no. 4 (2021): 162–71. http://dx.doi.org/10.21822/2073-6185-2020-47-4-162-171.

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Objective. The objective of the research is to design strutted beams that have optimal parameters for metal consumption.Methods. A mathematical simulation of the operation of a strutted beam with elastic support in the middle of the span is used. The calculation formulas are obtained based on the solution of the corresponding differential equation of the transverse bend of the upper crossbar of the strutted beam.Results. A method has been developed to optimize the design parameters of a single-strut beam. Constructive calculation schemes are given, as well as formulas that allow assigning desi

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12

Gurko, A. A. "Optimization of Magnetron Transformer Parameters." Telecommunications and Radio Engineering 52, no. 12 (1998): 59–64. http://dx.doi.org/10.1615/telecomradeng.v52.i12.130.

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13

Kazanskiy, N. L., I. S. Stepanenko, A. I. Khaimovich, S. V. Kravchenko, E. V. Byzov, and M. A. Moiseev. "Injectional multilens molding parameters optimization." Computer Optics 40, no. 2 (2016): 203–14. http://dx.doi.org/10.18287/2412-6179-2016-40-2-203-214.

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14

Barnabas, Godwin. "Parameters Optimization in Friction Surfacing." Chemical and Materials Engineering 2, no. 6 (2014): 127–36. http://dx.doi.org/10.13189/cme.2014.020601.

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15

Bhamare, Piyush. "Optimization Parameters of Disc Brake." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2657–62. http://dx.doi.org/10.22214/ijraset.2021.37821.

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Abstract: In recent time due to high performance of electronic component the heat generation is increasing drastically. Due to this scenario heat dissipation becomes a major issue in efficiency promation and stable operation. Silicon based microchannel heat sink fabricated using semiconductor production technique plays important role in cooling devices. The effect of the thermophysical properties of working fluids on the performance of microchannel is tested or we can say investigated. For this purpose the different working fluids are selected. water, hepthane, ammonia, methanol, and ethanol.

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16

Mostovenko, O., S. Kovalov, and A. Zolotova. "OPTIMIZATION OF ENERGY FIELD PARAMETERS." Modern problems of modeling 22 (June 16, 2021): 96–103. http://dx.doi.org/10.33842/22195203/2021/22/96/103.

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17

Lozhechnikov, E. B. "Optimization of powder rolling parameters." Soviet Powder Metallurgy and Metal Ceramics 24, no. 10 (1985): 758–60. http://dx.doi.org/10.1007/bf00797460.

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18

Lyon, J. F., L. P. Ku, P. Garabedian, L. El-Guebaly, and L. Bromberg. "Optimization of Stellarator Reactor Parameters." Fusion Science and Technology 47, no. 3 (2005): 414–21. http://dx.doi.org/10.13182/fst05-a723.

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19

Yeshak, Mariamawit Y., Robert Burman, Camilla Eriksson, and Ulf Göransson. "Optimization of cyclotide extraction parameters." Phytochemistry Letters 5, no. 4 (2012): 776–81. http://dx.doi.org/10.1016/j.phytol.2012.09.001.

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20

Di Pierro, Michele, and Ron Elber. "Automated Optimization of Potential Parameters." Journal of Chemical Theory and Computation 9, no. 8 (2013): 3311–20. http://dx.doi.org/10.1021/ct400313n.

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21

Sandström, R. "Merit parameters in materials optimization." Materials & Design 13, no. 3 (1992): 131–37. http://dx.doi.org/10.1016/0261-3069(92)90221-3.

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22

Yi-Cherng, Yeh, and Hsu Deh-Shiu. "Structural optimization with fuzzy parameters." Computers & Structures 37, no. 6 (1990): 917–24. http://dx.doi.org/10.1016/0045-7949(90)90005-m.

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23

Di Pierro, Michele, and Ron Elber. "Automated Optimization of Potential Parameters." Biophysical Journal 106, no. 2 (2014): 404a. http://dx.doi.org/10.1016/j.bpj.2013.11.2277.

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24

Sharma, H., B. Jain, and Z. Ahmad. "Optimization of submerged vane parameters." Sādhanā 41, no. 3 (2016): 327–36. http://dx.doi.org/10.1007/s12046-016-0477-8.

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25

Roeske, John C., George T. Y. Chen, Robert W. Atcher, and Ralph R. Weichselbaum. "Optimization of parameters in radioimmunotherapy." International Journal of Radiation Oncology*Biology*Physics 19 (January 1990): 268. http://dx.doi.org/10.1016/0360-3016(90)90925-a.

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26

Weltin, E. E. "Direct optimization of nonlinear parameters." International Journal of Quantum Chemistry 9, S9 (2009): 337–41. http://dx.doi.org/10.1002/qua.560090842.

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27

Wang, Jin Yang, Guo Min Cui, and Fu Yu Peng. "Integration of Distributing Energy System II: Optimization." Advanced Materials Research 512-515 (May 2012): 1151–55. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.1151.

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The optimization of the Distributed Energy System can improve the total energy utilization efficiency greatly and achieve energy saving. Based on the digital simulation software introduced by Part Ⅰ, the optimizations of matching parameters and structure can be achieved. The example results showed that numerical simulation can realize the optimization of matching parameters and economic operation parameters for the parts and the whole system, and the design result with the highest efficiency is obtained.

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28

Li, Jian Guang, Ying Xue Yao, Dong Gao, Chang Qing Liu, and Zhe Jun Yuan. "Cutting Parameters Optimization by Using Particle Swarm Optimization (PSO)." Applied Mechanics and Materials 10-12 (December 2007): 879–83. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.879.

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Cutting parameters play an essential role in the economics of machining. In this paper, particle swarm optimization (PSO), a novel optimization algorithm for cutting parameters optimization (CPO), was discussed comprehensively. First, the fundamental principle of PSO was introduced; then, the algorithm for PSO application in cutting parameters optimization was developed; thirdly, cutting experiments without and with optimized cutting parameters were conducted to demonstrate the effectiveness of optimization, respectively. The results show that the machining process was improved obviously.

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29

Wang, Pengfei, Run Luo, Jiashuang Wan, Xinyu Wei, and Fuyu Zhao. "ICONE23-1080 OPTIMIZATION OF MECHANICAL SHIM CONTROL SYSTEM PARAMETERS FOR AP1000 REACTOR USING PARTICLE SWARM OPTIMIZATION." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_43.

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30

Deng, Chao, Yao Xiong, Yuan Hang Wang, and Jun Wu. "Machining Process Parameters Optimization Based on Grid Optimization Algorithm." Advanced Materials Research 562-564 (August 2012): 2021–25. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2021.

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Machining process parameters directly affect the machining quality and efficiency of heavy-duty CNC machine tools, selecting correctly machining process parameters can improve the machine’s machining performance effectively. This paper presents a machining process parameters optimization method based on grid optimization algorithm for heavy-duty CNC machine tools. In this method, a multi-objective optimization model will be established, which considers not only the linear constrains of machining process parameters, such as machining time and machining cost, but also the non-linear constrain, s

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31

Nama, Sukanta, Apu Kumar Saha, and Sima Ghosh. "Parameters Optimization of Geotechnical Problem Using Different Optimization Algorithm." Geotechnical and Geological Engineering 33, no. 5 (2015): 1235–53. http://dx.doi.org/10.1007/s10706-015-9898-0.

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32

Zainal, Nurezayana, Azlan Mohd Zain, Nor Haizan Mohamed Radzi, and Muhamad Razib Othman. "Glowworm swarm optimization (GSO) for optimization of machining parameters." Journal of Intelligent Manufacturing 27, no. 4 (2014): 797–804. http://dx.doi.org/10.1007/s10845-014-0914-7.

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33

Sreeraj, P., T. Kannan, and Subhashis Maji. "Optimization of GMAW Process Parameters Using Particle Swarm Optimization." ISRN Metallurgy 2013 (January 22, 2013): 1–10. http://dx.doi.org/10.1155/2013/460651.

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To improve the corrosion-resistant properties of carbon steel cladding process is usually used. It is a process of depositing a thick layer of corrosion resistant material-over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding, these properties can be achieved with minimum cost. The main problem faced in cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental stud

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34

Huang, Shixin, Kedao Zhang, Hongmei Li, and Xiangjian Chen. "Application of Improved Monarch Butterfly Optimization for Parameters’ Optimization." Mathematical Problems in Engineering 2023 (January 11, 2023): 1–10. http://dx.doi.org/10.1155/2023/1348624.

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The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing

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35

Hao-Sheng Lu, Hao-Sheng Lu, Yan Lu Hao-Sheng Lu, Hui-Xin Li Yan Lu, et al. "Optimization Method of CNC Machining Parameters Based on Digital Twin." 電腦學刊 35, no. 3 (2024): 309–25. http://dx.doi.org/10.53106/199115992024063503021.

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<p>This article mainly discusses the optimization methods of machining parameters for CNC machine tools. Firstly, based on the functional requirements and application scenarios of CNC machining, three-dimensional modeling and motion simulation of twin machine tools are achieved, and a data-driven cutting process model is constructed. Then, in the actual production process, in addition to ensuring the normal processing of physical equipment such as CNC machine tools, the design of data perception schemes and the layout of data acquisition equipment during the processing were also complete

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36

Ebissa, G. K. Dr. K. S. Hari Prasad. "Estimation of open channel flow parameters by using optimization techniques." INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH Volume 5 | Issue 2 | May 2017, Volume 5 | Issue 2 | May 2017 (2017): Page Number(s) — 1049–1073. https://doi.org/10.5281/zenodo.583720.

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Open channel flow parameter estimation is an inverse problem, which involves the prediction of a function within a domain, given an error criterion with respect to a set of observed data. Various numerical methods have been developed to estimate open channel flow parameters. For this study, Genetic Algorithm optimization technique is selected. Because of its inherent characteristics, Genetic Algorithm optimization technique avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. The present study involves estimation of open c

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37

Niu, Xiang Jie. "The Optimization for PID Controller Parameters Based on Genetic Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 4102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4102.

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as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID pa

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38

Fucheng, D., D. Jingen, Y. Ying, W. Lihua, and Y. Wei. "Gravel pre-packed screen development and filled parameters optimization research." "Proceedings" of "OilGasScientificResearchProjects" Institute, SOCAR, no. 4 (December 30, 2012): 20–29. http://dx.doi.org/10.5510/ogp20120400130.

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39

Wei, Zhifang, and Fang Wang. "Overall Parameters Design and Optimization System for Gun and Bullet." International Journal of Materials, Mechanics and Manufacturing 6, no. 1 (2018): 1–7. http://dx.doi.org/10.18178/ijmmm.2018.6.1.337.

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40

Aruna, M. "Optimization of Parameters for Student Assessment Using Response Surface Methodology." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1492–97. http://dx.doi.org/10.5373/jardcs/v11sp10/20192994.

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41

M, Aruna, and Rashmi Rani. "Optimization of Parameters for Student Assessment Using Response Surface Methodology." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (2019): 540–45. http://dx.doi.org/10.5373/jardcs/v11sp11/20193064.

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42

Dr.V.Lakshminarayanan. "Optimization of 36cm2 Effective Area on Serpentine Flow Channel of PEMFC." International Journal of Trend in Scientific Research and Development 1, no. 4 (2017): 481–86. https://doi.org/10.31142/ijtsrd1183.

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The Proton Exchange Membrane Fuel Cell PEMFC is an electrochemical device and its performance depends on the operating and design parameters. In this paper, optimization of design parameters like flow channel design, number of flow path, channel depth and width, cross section of the flow channel and operating parameters as operating pressure, temperature, relative humidity, mass flow rate of the reactant gases and stoichiometric ratio of the reactants on serpentine flow channel of 36 cm2 effective area of the PEMFC was considered. Creo software used for modeling and CFD software packages used

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43

Naeimi, Mohammad Reza, Karim Abbaszadeh, and Reza Nasiri-Zarandi. "Torque ripples reduction in a synchronous reluctance motor by rotor parameters optimization." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 6 (2021): 1053–66. http://dx.doi.org/10.1108/compel-11-2020-0362.

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Purpose High torque ripple is the significant challenge of the synchronous reluctance machine in household electric appliances, electric vehicles and so on. This paper aims to present an optimized design of a synchronous reluctance rotor structure to reduce the torque ripple with improving the average torque by the particle swarm optimization (PSO) algorithm. Design/methodology/approach The optimization of rotor geometries has been investigated. Most of the rotor parameters such as the width of iron parts, the width of barriers along d and q axes and the endpoint angle of barriers are optimize

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44

Liu, Xiang Ying, Hui Yan Jiang, and Feng Zhen Tang. "Parameters Optimization in SVM Based-On Ant Colony Optimization Algorithm." Advanced Materials Research 121-122 (June 2010): 470–75. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.470.

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In this paper ACO (Ant Colony Optimization) algorithm, which is a well-known intelligent optimization method, is applied to selecting parameters for SVM.ACO has the characteristics of positive feedback, parallel mechanism and distributed computation. This paper gives comparison of ACO-SVM, PSO-SVM whose parameters are determined by particle swarm optimization algorithm, and traditional SVM whose parameters are decided through trial and error. The experimental results on real-world datasets show that this proposed method avoids randomness and subjectivity in the traditional SVM. Additionally it

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45

Song, Jin Bao, Long Ye, Qin Zhang, and Jian Ping Chai. "Video Redirection Parameters Optimization Based on Hierarchical Genetic Optimization Strategy." Applied Mechanics and Materials 668-669 (October 2014): 1152–56. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1152.

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In the process of particle filter, it can produce the problem of particle degeneration, and the amount of computation is very big. In order to solve these two problems, this paper presents a method for video redirection parameters optimization based on hierarchical genetic optimization strategy. This paper adopts hierarchical mutation operator and local crossover operator. The result shows that the hierarchical genetic optimization algorithm which is used in video tracking has a very good effect.

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46

Hu, Ze Hao, Wei Wei, Juan Liu, and Kun Liu. "Application of Optimization Algorithms in Injection Molding Process Parameters Optimization." Materials Science Forum 704-705 (December 2011): 183–90. http://dx.doi.org/10.4028/www.scientific.net/msf.704-705.183.

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In this paper, the numerical simulation and calculation of injection molding process are taken in the Moldflow software. The BP artificial neural network combining with the orthogonal experiment design method is used to set up the injection molding process agent model, genetic algorithms are applied to realize global optimization, finally, the optimal combination of process parameters of each quality indicators is obtained.

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47

Tamilarasan, A., A. Renugambal, T. Mohan, et al. "Optimization of roller burnishing process parameters using lion optimization algorithm." IOP Conference Series: Materials Science and Engineering 390 (July 30, 2018): 012063. http://dx.doi.org/10.1088/1757-899x/390/1/012063.

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48

Haddad, Sofiane, Mohamed Benghanem, Belqees Hassan, Ammar Soukkou, Badis Lekouaghet, and Yassine Soukkou. "Parameters optimization of PEMFC model based on gazelle optimization algorithm." International Journal of Hydrogen Energy 87 (October 2024): 214–26. http://dx.doi.org/10.1016/j.ijhydene.2024.09.027.

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49

Sidorenko, G., and A. AlJamil. "WIND POWER PLANT PARAMETERS OPTIMIZATION MODEL." EurasianUnionScientists 5, no. 1(82) (2021): 35–41. http://dx.doi.org/10.31618/esu.2413-9335.2021.5.82.1228.

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A technical and economic model of a wind power plant has been developed. Parameterization was carried out and the main parameters of the wind power plant were identified. Statistical relationships of parameters with capital investments in wind turbines and costs of their operation and maintenance were established. The criterion for the search for optimal parameters is taken as the minimum discounted unit costs for the production of electrical energy for the economic life of the wind power plant. To do this, the electricity production in a wind power plant must be maximized by reducing the so-c

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50

Andrey Grabovskiy, Andrey, Mykola Tkachuk, Maryna Bondarenko, et al. "OPTIMIZATION OF PARAMETERS OF CAM MECHANISMS." Bulletin of the National Technical University «KhPI» Series: Engineering and CAD, no. 7(1332) (October 9, 2019): 47–54. http://dx.doi.org/10.20998/2079-0775.2019.7.08.

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