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Journal articles on the topic 'Parameter optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pres

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3

Prinz, Astrid. "Neuronal parameter optimization." Scholarpedia 2, no. 1 (2007): 1903. http://dx.doi.org/10.4249/scholarpedia.1903.

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4

Lawal, S. L., and S. A. Afolalu. "Effect of Welding Process Parameters on the Mechanical Properties of TIG and MIG Welds in HSS X65 Pipe-A Review." IOP Conference Series: Earth and Environmental Science 1322, no. 1 (2024): 012009. http://dx.doi.org/10.1088/1755-1315/1322/1/012009.

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Abstract The study focused on the importance of the different welding parameters on the mechanical behavior of High Strength Steel (HSS) X65 steel pipes by reviewing the advantages of parameter optimization for the Tungsten Inert Gas (TIG) - Metal Inert Gas (MIG) welding processes. The parameters considered in the study include welding speed, welding current, welding voltage and gas flowrate of the welding. The effects of improper selection and parameter optimizations were highlighted and illustrated using different metallurgical and mechanical instances. The outcome of the study indicates tha

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Ng, Chuan Huat, and Mohd Khairulamzari Hamjah. "Welding Parameter Optimization of Surface Quality by Taguchi Method." Applied Mechanics and Materials 660 (October 2014): 109–13. http://dx.doi.org/10.4028/www.scientific.net/amm.660.109.

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An experimental study of GTAW was conducted to determine the optimization of weld parameters on the droplet formation in the surface quality of weld pools. These optimization investigations consisted of welding current, welding speed and feed rate. The strength and surface quality of weld pool were measured for each specimen after the welding parameter optimizations and the effect of these parameters on droplet formation were researched. To consider these quality characteristics together in the selection of welding parameters, the Orthogonal Array of Taguchi method is adopted to analyze the ef

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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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7

Kawade, Dr Mahesh M., Prasanna Raut, and Devakant Baviskar. "Exploiting Tool Toughness through Turning Process Parameter Optimization." International Journal of Research Publication and Reviews 5, no. 2 (2024): 364–71. http://dx.doi.org/10.55248/gengpi.5.0224.0408.

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8

Adil, H., A. A. Koser, M. S. Qureshi, and A. Gupta. "Sleep quality assessment by parameter optimization." Journal of Physics: Conference Series 2070, no. 1 (2021): 012013. http://dx.doi.org/10.1088/1742-6596/2070/1/012013.

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Abstract Sleep quality measurement is a complex process requires large number of parameters to monitor sleep and sleep cycles. The Gold Standard Polysomnography (PSG) parameters are considered as standard parameters for sleep quality measurement. In the PSG process, number of monitoring parameters are involved for that large number of sensors are used which makes this process complex, expensive and obtrusive. There is need to find optimize parameters which are directly involve in providing accurate information about sleep and reduce the process complexity. Our Parameter Optimization method is

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Kitamura, Makoto, Masahiro Umeda, Toshihiro Higuchi, Shouji Naruse, and Chuuzou Tanaka. "465. Optimization of a parameter in functional-MRI paramete." Japanese Journal of Radiological Technology 50, no. 8 (1994): 1380. http://dx.doi.org/10.6009/jjrt.kj00003326264.

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10

Ma, Kai, Yu-Bin Gao, Yan Tao, Wen-Tao Wang, and Shuai-Chen Wu. "A Multi-objective Control Method for Structural Static Displacement Based on Projection Parameters Sorting Method." Journal of Physics: Conference Series 3004, no. 1 (2025): 012048. https://doi.org/10.1088/1742-6596/3004/1/012048.

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Abstract In the field of structural optimization, reasonable selection of parameters and obtaining optimized solutions are key issues. This paper proposes a multi-objective optimization method for static displacement based on the Projection Parameter Sorting Method (PPSM). This method sorts parameters based on the projection characteristics of the parameter sensitivity vector, and uses the Epsilon algorithm and improved Neumann series to calculate the structural static sensitivity. Through iteration, combinations are selected and parameters are corrected according to a given number of paramete

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Zala, Cedric A., and John M. Ozard. "Estimation of Geoacoustic Parameters from Narrowband Data Using a Search-Optimization Technique." Journal of Computational Acoustics 06, no. 01n02 (1998): 223–43. http://dx.doi.org/10.1142/s0218396x98000168.

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Geoacoustic parameters were estimated for vertical array data from the matched-field inversion benchmark data sets. Separate inversions were performed for narrowband data at 25 Hz, 50 Hz and 75 Hz, using a matching function consisting of the incoherent sum of the Bartlett outputs for the five vertical arrays at ranges of 1, 2, 3, 4 and 5 km. Parameter estimation was performed using a parabolic equation sound propagation algorithm to generate the replica fields, and a search-optimization technique to obtain estimates of the optimized parameter values. This technique involved an initial search s

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Sumata, Hiroshi, Frank Kauker, Michael Karcher, and Rüdiger Gerdes. "Covariance of Optimal Parameters of an Arctic Sea Ice–Ocean Model." Monthly Weather Review 147, no. 7 (2019): 2579–602. http://dx.doi.org/10.1175/mwr-d-18-0375.1.

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Abstract The uniqueness of optimal parameter sets of an Arctic sea ice simulation is investigated. A set of parameter optimization experiments is performed using an automatic parameter optimization system, which simultaneously optimizes 15 dynamic and thermodynamic process parameters. The system employs a stochastic approach (genetic algorithm) to find the global minimum of a cost function. The cost function is defined by the model–observation misfit and observational uncertainties of three sea ice properties (concentration, thickness, drift) covering the entire Arctic Ocean over more than two

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Vohra, Nilesh M. "Optimization of Cutting Parameter in Edm Using Taguchi Method." International Journal of Scientific Research 2, no. 1 (2012): 90–95. http://dx.doi.org/10.15373/22778179/jan2013/32.

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14

Aliemeke, Blessing Ngozi Goodluck, Henry Adimabuah Okwudibe, Asunumeh Sunday Ososomi, and Braimah Dirisu. "Taguchi Optimization of Screw Flight Bending Operation." ABUAD Journal of Engineering Research and Development (AJERD) 7, no. 1 (2024): 2014–220. http://dx.doi.org/10.53982/ajerd.2024.0701.22-j.

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The optimizations of screw flight bending operating parameters have been successfully carried out. The optimization of the screw flight bending operation, aims at determining optimal values for key parameters using Taguchi Design and Genetic Algorithm (GA) optimization tools. The parameters investigated include bending radius, diameter of screw, flight thickness, and bending force. Through the systematic application of Taguchi methodology and GA optimization, optimal values of 79.99 mm for bending radius, 69.997 mm for diameter of screw, 5.005 mm for flight thickness, and 232.62 N for bending

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15

Jiang, Jiawei, Yanhong Wu, Hongyan Wang, Yakun Lv, Lei Qiu, and Daobin Yu. "Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization." Electronics 8, no. 2 (2019): 160. http://dx.doi.org/10.3390/electronics8020160.

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Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine i

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16

Liu, Qi-yang, Jiang-wei Geng, Yang Wang, Yong Ge, and Qi Bao. "Multi-Parameter Damper Optimization of Cable-Stayed Bridge Considering Energy Dissipation." Journal of Physics: Conference Series 2541, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2541/1/012002.

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Abstract The parameters optimization of a viscous damper is carried out with a cable-stayed bridge with a single cable plane and a single tower. Based on five optimization parameters and four optimization functions of damper energy dissipation and structure, the relationship between each function and damper parameters is obtained. Finally, the spatial distribution surface of the multi-parameter objective optimization function is constructed, and the final optimization result is obtained. The optimization process shows that the reasonable viscous damper parameters can effectively inhibit the vi

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17

INOUE, Kazuya, and Mariko SUZUKI. "PARAMETER OPTIMIZATION USING SWARM INTELLIGENCE." Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) 74, no. 2 (2018): I_33—I_44. http://dx.doi.org/10.2208/jscejam.74.i_33.

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18

VILORIA, EDGAR. "Wire bond parameter optimization study." Journal of Electronics Manufacturing 04, no. 04 (1994): 217–22. http://dx.doi.org/10.1142/s0960313194000237.

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19

Amyot, Joseph R., and Gerard van Blokland. "Parameter optimization with ACSL models." SIMULATION 49, no. 5 (1987): 213–18. http://dx.doi.org/10.1177/003754978704900505.

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A method whereby a parameter optimization program, written in FORTRAN, can be used in conjunction with ACSL (Advanced Continuous Simulation Language) models of dynamic systems is described. The optimization of a projectile's trajectory is used as an example.

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Amyot, Joseph R., and van Blokland Gerard. "Parameter Optimization with ACSL Models." SIMULATION 49, no. 5 (1987): 213–18. http://dx.doi.org/10.1177/003754978904900505.

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21

Hestermeyer, Thorsten, Eckehard Münch, and Erika Schäfer. "Model-Based Online Parameter Optimization." IFAC Proceedings Volumes 37, no. 14 (2004): 193–98. http://dx.doi.org/10.1016/s1474-6670(17)31103-5.

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22

Valente, Robertt A. F., António Andrade-Campos, José F. Carvalho, and Paulo S. Cruz. "Parameter identification and shape optimization." Optimization and Engineering 12, no. 1-2 (2010): 129–52. http://dx.doi.org/10.1007/s11081-010-9126-y.

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23

Seo, Y. K., S. Yu, and A. Gafurov. "CV-joint remanufacturing parameter optimization." International Journal of Automotive Technology 15, no. 4 (2014): 603–10. http://dx.doi.org/10.1007/s12239-014-0063-1.

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24

Szczerbicka, Rainer Barton, Helena. "INDUCTIVE LEARNING FOR PARAMETER OPTIMIZATION." Cybernetics and Systems 31, no. 5 (2000): 469–90. http://dx.doi.org/10.1080/01969720050045985.

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25

Kout, Alexander, and Heinrich Müller. "Parameter optimization for spray coating." Advances in Engineering Software 40, no. 10 (2009): 1078–86. http://dx.doi.org/10.1016/j.advengsoft.2009.03.001.

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26

Yum, Bong-Jin, and Sun-Woo Ko. "On parameter design optimization procedures." Quality and Reliability Engineering International 7, no. 1 (1991): 39–46. http://dx.doi.org/10.1002/qre.4680070110.

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27

Eylen, Tayfun, P. Erhan Eren, and Altan Koçyiğit. "Data-driven alarm parameter optimization." Computers & Chemical Engineering 196 (May 2025): 109041. https://doi.org/10.1016/j.compchemeng.2025.109041.

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28

Li, Guang Chun, Ping Yan, and Sui Er Wang. "Study on Optimal Method for PID Parameter Based on Artificial Bee Colony Algorithm." Applied Mechanics and Materials 624 (August 2014): 454–59. http://dx.doi.org/10.4028/www.scientific.net/amm.624.454.

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The PID parameter optimization has been a hot topic of control system research. Artificial bee colony algorithm is used to optimize the PID parameters of control system, and gives the PID parameter optimization process of the traditional artificial bee colony algorithm. Aiming at the optimization problem of long time, the improved artificial bee colony algorithm was put forward. The simulation results show that the improved artificial bee colony algorithm can significantly shorten the time of PID parameter optimization, control and better optimization accuracy.

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29

Sumata, H., F. Kauker, R. Gerdes, C. Köberle, and M. Karcher. "A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model." Ocean Science 9, no. 4 (2013): 609–30. http://dx.doi.org/10.5194/os-9-609-2013.

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Abstract. Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean–sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (μGA) as an example of a stochastic approach. The optimizations were performed by minimizing a cost function composed of model–data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations wer

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Liu, M., C. Li, Y. Lei, and P. Dai. "PID control parameter optimization of servo system based on particle swarm optimization." Journal of Physics: Conference Series 2460, no. 1 (2023): 012166. http://dx.doi.org/10.1088/1742-6596/2460/1/012166.

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Abstract Thermal power servo system is a typical servo system, the controller as the main component, its parameters decide the control performance of the servo system, The traditional Z-N PID parameter setting method is often unable to achieve the best control performance. The author proposes a particle swarm optimization (PSO) algorithm, which uses the integrated performance index time and error absolute value product integral ITAE as the fitness function and applies it to PID control parameter optimization. The model is built in MATLAB/Simulink for analysis and comparision. The results displ

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Pan, Jeng-Shyang, Cheng Yang, Fanjia Meng, Yuxin Chen, and Zhenyu Meng. "A parameter adaptive DE algorithm on real-parameter optimization." Journal of Intelligent & Fuzzy Systems 38, no. 5 (2020): 5775–86. http://dx.doi.org/10.3233/jifs-179665.

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32

Radke, F., and R. Isermann. "A parameter-adaptive PID-controller with stepwise parameter optimization." Automatica 23, no. 4 (1987): 449–57. http://dx.doi.org/10.1016/0005-1098(87)90074-4.

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33

Iwata, Tatsuya, Yuki Okura, Maaki Saeki, and Takefumi Yoshikawa. "Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization." Sensors 24, no. 9 (2024): 2941. http://dx.doi.org/10.3390/s24092941.

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This study proposes an optimization method for temperature modulation in chemiresistor-type gas sensors based on Bayesian optimization (BO), and its applicability was investigated. As voltage for a sensor heater, our previously proposed waveform was employed, and the parameters determining the voltage range were optimized. Employing the Bouldin–Davies index (DBI) as an objective function (OBJ), BO was utilized to minimize the DBI calculated from a feature matrix built from the collected data followed by pre-processing. The sensor responses were measured using five test gases with five concentr

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Zhu, Yuanyuan, Shijie Su, Yuchen Qian, Yun Chen, and Wenxian Tang. "Parameter Optimization for Ship Antiroll Gyros." Applied Sciences 10, no. 2 (2020): 661. http://dx.doi.org/10.3390/app10020661.

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Ship antiroll gyros are a type of equipment used to reduce ships’ roll angle, and their parameters are related to the parameters of a ship and wave, which affect gyro performance. As an alternative framework, we designed a calculation method for roll reduction rate and considered random waves to establish a gyro parameter optimization model, and we then solved it through the bacteria foraging optimization algorithm (BFOA) and pattern search optimization algorithm (PSOA) to obtain optimal parameter values. Results revealed that the two methods could effectively reduce the overall mass and floor

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35

Wu, Xinrong, Shaoqing Zhang, Zhengyu Liu, Anthony Rosati, Thomas L. Delworth, and Yun Liu. "Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model." Monthly Weather Review 140, no. 12 (2012): 3956–71. http://dx.doi.org/10.1175/mwr-d-11-00298.1.

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Abstract Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a

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Töpel, Eric, Alexander Fuchs, Kay Büttner, Michael Kaliske, and Günther Prokop. "Machine-Learning-Based Design Optimization of Chassis Bushings." Vehicles 6, no. 1 (2023): 1–21. http://dx.doi.org/10.3390/vehicles6010001.

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In this work, a method is developed for the component design of chassis bushings with contoured inner cores, aided by artificial neural networks (ANNs) and design optimization. First, a model of a physical chassis bushing is generated using the finite element method (FEM). To determine the material parameters of the material model, a material parameter optimization is conducted. Based on the bushing model, different samples for a design study are generated using the design of experiments method. Due to invalid areas of the geometrical model definitions, constraints are established and the desi

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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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John, Christian, Dietmar Tutsch, Thomas Lepich, Bernard Beitz, and Reinhard Möller. "A Criteria Transformation Approach to Pattern Matching based on Non-Linear Parameter Optimization." Journal of Intelligent Systems 24, no. 2 (2015): 249–63. http://dx.doi.org/10.1515/jisys-2014-0114.

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AbstractThis paper presents a concept for pattern matching based on a parameter optimization system for approximative numerical calculation of some parameter combination under soft and hard constraints. The concept uses a non-linear parameter optimization method with an iterative variation of parameters. The paper focuses on the information modeling process to migrate problem-domain specific criteria into optimization-compatible objects suitable for a standardized parameter optimization procedure. A step-by-step transformation process is presented and implemented in object-oriented programming

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HOTHAZIE, Mihai-Vladut, Georgiana ICHIM, and Mihai-Victor PRICOP. "Development and validation of constraints handling in a Differential Evolution optimizer." INCAS BULLETIN 12, no. 1 (2020): 59–66. http://dx.doi.org/10.13111/2066-8201.2020.12.1.6.

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Research work requires independent, portable optimization tools for many applications, most often for problems where derivability of objective functions is not satisfied. Differential evolution optimization represents an alternative to the more complex, encryption based genetic algorithms. Various packages are available as freeware, but they lack constraints handling, while constrained optimizations packages are commercially available. However, the literature devoted to constraints treatment is significant and the current work is devoted to the implementation of such an optimizer, to be applie

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40

Cohal, Viorel. "A Simulation of Spot Welding Process." Applied Mechanics and Materials 657 (October 2014): 226–30. http://dx.doi.org/10.4028/www.scientific.net/amm.657.226.

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The paper presents the optimization of spot welding parameters using offline simulation. The procedure of making simulation with SORPAS® is similar to the procedure of doing practical welding process, which can be divided into the following three steps:Data preparation - the materials and geometries of the workpieces and electrodes are defined, the type of welding machine is selected and the process parameters are specified.Running simulation of welding - the parts are welded in the selected welding machine with the specified process parameter settings. The simulations can be carried out in fo

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41

Rao, R. V., and P. J. Pawar. "Grinding process parameter optimization using non-traditional optimization algorithms." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 224, no. 6 (2009): 887–98. http://dx.doi.org/10.1243/09544054jem1782.

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Hao, Shangqing, Xuewen Wang, Jiacheng Xie, and Zhaojian Yang. "Rigid framework section parameter optimization and optimization algorithm research." Transactions of the Canadian Society for Mechanical Engineering 43, no. 3 (2019): 398–404. http://dx.doi.org/10.1139/tcsme-2018-0085.

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This article compares the optimization algorithms included with ANSYS Software for optimizing the dimensions of a large steel framework to minimize weight while maintaining stiffness. A finite element model of the structure was prepared, and the section parameters were optimized using the sub-problem and first-order algorithms. These reduce the weight of the structure by 33.8%. The sub-problem algorithm and the first-order algorithm are explained from the rationale, iteration method, and convergence criterion. According to the optimized result, these two algorithms were compared. The results s

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Chenthil Jegan, Thankaraj Mariapushpam, and Durairaj Ravindran. "Electrochemical machining process parameter optimization using particle swarm optimization." Computational Intelligence 33, no. 4 (2017): 1019–37. http://dx.doi.org/10.1111/coin.12139.

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Zhang, Lixiu, Da Teng, and Qiang Shen. "Parameter Optimization of Drilling Based on Biogeography-based Optimization." IOP Conference Series: Earth and Environmental Science 170 (July 2018): 022180. http://dx.doi.org/10.1088/1755-1315/170/2/022180.

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Wu, Dongmei, and Hao Gao. "An Adaptive Particle Swarm Optimization for Engine Parameter Optimization." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 88, no. 1 (2016): 121–28. http://dx.doi.org/10.1007/s40010-016-0320-y.

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Choi, Tae Jong, Chang Wook Ahn, and Jinung An. "An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization." Scientific World Journal 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/969734.

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Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control paramet

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Meng, Zhuo, Qin Sun, and Jin Feng Jiang. "Parameter Inversion of Thin-Walled Structure Using Numerical Optimization Approach." Advanced Materials Research 308-310 (August 2011): 1614–18. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1614.

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Based on the theory of structure impact dynamic and numerical optimization, application of SQP algorithm in nonlinear parameter inversion is demonstrated in this paper by combining the optimization software and finite element software. Parameter inversion of thin-walled cylinder subjected to axial impact load is studied. The material parameters of thin-walled cylinder are obtained from inversion; besides, the whole process of numerical optimization parameter inversed method is demonstrated.

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Wischnewski, Kevin J., Florian Jarre, Simon B. Eickhoff, and Oleksandr V. Popovych. "Exploring dynamical whole-brain models in high-dimensional parameter spaces." PLOS One 20, no. 5 (2025): e0322983. https://doi.org/10.1371/journal.pone.0322983.

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Personalized modeling of the resting-state brain activity implies the usage of dynamical whole-brain models with high-dimensional model parameter spaces. However, the practical benefits and mathematical challenges originating from such approaches have not been thoroughly documented, leaving the question of the value and utility of high-dimensional approaches unanswered. Studying a whole-brain model of coupled phase oscillators, we proceeded from low-dimensional scenarios featuring 2–3 global model parameters only to high-dimensional cases, where we additionally equipped every brain region with

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Lee, Yeon-Seung, and Young-Bok Choi. "Hull Form Optimization Based on From Parameter Design." Journal of the Society of Naval Architects of Korea 46, no. 6 (2009): 562–68. http://dx.doi.org/10.3744/snak.2009.46.6.562.

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Sun, Wen, Xiang Yu Kong, Qun Yang, and Fang Zhang. "Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization." Applied Mechanics and Materials 448-453 (October 2013): 2511–15. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2511.

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Abstract:

A parameter identification method for generator speed governor system, which combines decoupling parameter identification and overall recognition with measured data, was proposed in the paper. The method bases on particle swarm optimization, and takes parameter identification as a parameters optimization problem under evaluation function. According to an intelligent optimization algorithms evolutionary strategy, the individual's status is continuously adjusted until the identification system and actual system output error is sufficiently small. Case studies show that the proposed method can be

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