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Journal articles on the topic 'Combination optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Nan, Yang. "An Improved Ant Colony Optimization Algorithm Based on Immunization Strategy." Advanced Materials Research 490-495 (March 2012): 66–70. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.66.

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Ant colony optimization has been become a very useful method for combination optimization problems. Based on close connections between combination optimization and continuous optimization, nowadays some scholars have studied to apply ant colony optimization to continuous optimization problems, and proposed some continuous ant colony optimizations. To improve the performance of those continuous ant colony optimizations, here the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous Ant Colony Optimization, and the adaptive Cauchi mut

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Wang, Jun, Xurong Dong, Wei Fu, Di Yan, and Zengkai Shi. "Optimization and selection of Galileo triple-frequency carrier linear combination." Measurement and Control 54, no. 1-2 (2021): 116–28. http://dx.doi.org/10.1177/0020294020983386.

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The triple-frequency linear combination with a low noise, a long wavelength, and a weak ionosphere is beneficial to effectively eliminate or weaken the common errors, advance the reliability of cycle slip detection and repair, and speed up the convergence time of fixed ambiguity. By establishing the Galileo triple-frequency carrier linear combination model, three types of linear combinations are derived: Geometry-free (GF) combinations, minimum noise (MN) combinations, and ionosphere-free (IF) combinations. The geometric relationships of these linear combinations are displayed in the form of i

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Venu, Borigorla, L. Suvarna Raju, and K. Venkata Rao. "Multiobjective optimization of friction stir weldments of AA2014-T651 by teaching–learning-based optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 234, no. 6 (2019): 1146–55. http://dx.doi.org/10.1177/0954406219891755.

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This study focuses on optimization of process parameters, which may result in improved mechanical properties of the friction stir weldments of AA2014-T651. Plain taper and threaded taper cylindrical tool pin profiles were used for the study. A set of experiments was conducted at different levels of tool rotational and weld speeds using two tool pin profiles. Mechanical properties such as tensile strength, yield strength, impact strength, percentage of elongation, and hardness were measured. Objective functions are developed for the five mechanical properties in terms of input parameters. The i

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Huo, Jiuyuan, and Liqun Liu. "Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation." Computational Intelligence and Neuroscience 2020 (March 10, 2020): 1–23. http://dx.doi.org/10.1155/2020/8594727.

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Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze

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Lin, Yusheng, Zheng Sun, Alexandru Dadalau, and Alexander Verl. "Efficient Combination of Topology and Parameter Optimization." Open Journal of Optimization 03, no. 03 (2014): 19–25. http://dx.doi.org/10.4236/ojop.2014.33003.

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Pulkkinen, Otto I., Prson Gautam, Ville Mustonen, and Tero Aittokallio. "Multiobjective optimization identifies cancer-selective combination therapies." PLOS Computational Biology 16, no. 12 (2020): e1008538. http://dx.doi.org/10.1371/journal.pcbi.1008538.

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Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the sea

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7

Liu, Gia-Shie. "A combination method for reliability-redundancy optimization." Engineering Optimization 38, no. 4 (2006): 485–99. http://dx.doi.org/10.1080/03052150500465541.

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Patel G C, Manjunath, Prasad Krishna, Mahesh B. Parappagoudar, and Pandu Ranga Vundavilli. "Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms." International Journal of Swarm Intelligence Research 7, no. 1 (2016): 55–74. http://dx.doi.org/10.4018/ijsir.2016010103.

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The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights

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9

Dhanarasi, Gowtham, B. T. Krishna, and K. Padma Raju. "Design of Digital Differentiator Using a Combination of System Identification and Particle Swarm Optimization." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (2019): 131–36. http://dx.doi.org/10.5373/jardcs/v11i10/20193015.

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10

Tan, Wen Zhe, Xin Li, and Yue Ting Chai. "Policy-Combination Oriented Optimization for Supply Chain Simulation." Applied Mechanics and Materials 302 (February 2013): 646–51. http://dx.doi.org/10.4028/www.scientific.net/amm.302.646.

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Supply chain simulation is able to capture the dynamics and uncertainties in all kinds of supply chains and provide quantitative performance evaluations. In order to address the time consuming evaluation and large search space issues in supply chain simulation, this paper proposes a policy-combination oriented optimization approach to conduct decision makings. The approach begins with reducing the search space by relaxing the goal of optimization, and then refers to meta-heuristic searching methods to solve the main bi-level optimization problem. Lastly the key parameters are fine-tuned with w

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11

Mach, F. "Reduction of Optimization Problem by Combination of Optimization Algorithm and Sensitivity Analysis." IEEE Transactions on Magnetics 52, no. 3 (2016): 1–4. http://dx.doi.org/10.1109/tmag.2015.2500026.

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12

Zhang, Zhuo Qun, and Hong Nan Li. "Two-Level Optimization Method of Transmission Tower Structure Based on Ant Colony Algorithm." Advanced Materials Research 243-249 (May 2011): 5849–53. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.5849.

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This paper highlights a two-level optimization process combined with ant colony optimization algorithm (ACO)，which is applied to design the shape of the transmission tower structure, and compiles optimization design procedure for the solution of the structure shape with the MATLAB language. By comparing the different methods of the transmission tower structure optimization design, the ACO algorithm is demonstrated to be more accuracy and practical in dealing the complex optimizations problem, i.e. the combination of continuous variables and discrete variables. These calculation examples illust

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13

Yang, Yuliang, Yu Yang, Ying Sun, Jian Zeng, and Yunquan Zhang. "Performance Optimization for the Centre Axle Trailer Combination." SAE International Journal of Commercial Vehicles 10, no. 1 (2017): 236–44. http://dx.doi.org/10.4271/2017-01-0419.

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14

Zhang, Han, Rui Feng Guo, and Cong Geng. "Hierarchical Optimization Model of Cloud Manufacturing Services Combination." Applied Mechanics and Materials 464 (November 2013): 345–51. http://dx.doi.org/10.4028/www.scientific.net/amm.464.345.

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In this paper, a new mathematical combined model and the corresponding solution algorithm were proposed by analyzing the characteristics of services resource combination in cloud manufacturing. Aiming at avoiding problems of uncertainty, coarse-grained, diversity and dynamic in the process of services resource combination, a hierarchical model based on the hierarchical manufacturing implementation processes was firstly proposed. Then, quality of service (QoS) has been chosen to evaluate effects of services combination. Finally, a annealing algorithm was developed to solve the proposed model. S

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15

Vidarsson, Logi, Steven M. Conolly, Kelvin O. Lim, Garry E. Gold, and John M. Pauly. "Echo time optimization for linear combination myelin imaging." Magnetic Resonance in Medicine 53, no. 2 (2005): 398–407. http://dx.doi.org/10.1002/mrm.20360.

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16

Ouanes, Mohand, Mohammed Chebbah, and Ahmed Zidna. "Combination of two underestimators for univariate global optimization." RAIRO - Operations Research 52, no. 1 (2018): 177–86. http://dx.doi.org/10.1051/ro/2018013.

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In this work, we propose a new underestimator in branch and bound algorithm for solving univariate global optimization problems. The new underestimator is a combination of two underestimators, the classical one used in αBB method (see Androulakis et al. [J. Glob. Optim. 7 (1995) 337–3637]) and the quadratic underestimator developed in Hoai An and Ouanes [RAIRO: OR 40 (2006) 285–302]. We show that the new underestimator is tighter than the two underestimators. A convex/concave test is used to accelerate the convergence of the proposed algorithm. The convergence of our algorithm is shown and a s

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17

Deng, Yongbo, Zhenyu Liu, Yongshun Liu, and Yihui Wu. "Combination of topology optimization and optimal control method." Journal of Computational Physics 257 (January 2014): 374–99. http://dx.doi.org/10.1016/j.jcp.2013.09.033.

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18

Sun, Huibin, Neng Wan, Zhiyong Chang, and Rong Mo. "Approach to optimization of part machining service combination." International Journal of Advanced Manufacturing Technology 56, no. 5-8 (2011): 767–76. http://dx.doi.org/10.1007/s00170-011-3217-6.

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19

Song, Xiangzhong, Yue Huang, Hong Yan, Yanmei Xiong, and Shungeng Min. "A novel algorithm for spectral interval combination optimization." Analytica Chimica Acta 948 (December 2016): 19–29. http://dx.doi.org/10.1016/j.aca.2016.10.041.

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20

Wang, Qiang, Zhiyuan Guo, Jun Guo, and Gang Liu. "Optimization of Filter Combination for Query by Humming." Circuits, Systems, and Signal Processing 34, no. 10 (2015): 3373–93. http://dx.doi.org/10.1007/s00034-015-0021-9.

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21

Schoenmakers, Peter J., Anne Peeters, and Roderick J. Lynch. "Optimization of chromatographic methods by a combination of optimization software and expert systems." Journal of Chromatography A 506 (May 1990): 169–84. http://dx.doi.org/10.1016/s0021-9673(01)91576-6.

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22

Sumarno, Radiktyo Nindyo, Susatyo Handoko, and Mochammad Facta. "The Improvement of Electric Power Losses Using Bank Capacitor and Tap Changer With Shark Smell Algorithm." TEKNIK 41, no. 3 (2020): 212–18. http://dx.doi.org/10.14710/teknik.v41i3.24818.

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One way to optimize the transmission line is to reduce electrical power losses. Tap changers on power transformers and bank capacitors can be used to regulate the system voltage resulting in lower power losses in the transmission line. Determining the value of tap settings and bank capacitors in the planning process is challenging to do with certainty. It is generally carried out through a trial and error mechanism using the power flow method. Since the determination of tap settings and bank capacitors values is difficult to do with certainty, this research was carried out with optimization wi

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23

Fang, J., S. M. Smith, and I. Elishakoff. "Combination of anti-optimization and fuzzy-set-based analysis for structural optimization under uncertainty." Mathematical Problems in Engineering 4, no. 3 (1998): 187–200. http://dx.doi.org/10.1155/s1024123x98000787.

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An approach to the optimum design of structures, in which uncertainties with a fuzzy nature in the magnitude of the loads are considered, is proposed in this study. The optimization process under fuzzy loads is transformed into a fuzzy optimization problem based on the notion of Werners' maximizing set by defining membership functions of the objective function and constraints. In this paper, Werner's maximizing set is defined using the results obtained by first conducting an optimization through anti-optimization modeling of the uncertain loads. An example of a ten-bar truss is used to illustr

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24

Gao, Yao Dong, Wei Li, and Huan Yu Wang. "The Optimization Design of Combination Sleeve Based on ANSYS." Applied Mechanics and Materials 496-500 (January 2014): 688–91. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.688.

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The limitations of traditional methods of the optimization design of combination sleeve are pointed out in this paper. The features and steps of the optimization design by using ANSYS are introduced. The steps and features of applying ANSYS to optimize the combination sleeve are introduced detailed with an example, and the solution to the key problem is introduced. The method has a high value for application and promotion.

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25

Jamema, SwamidasV, Sherly Saju, UmeshM Shetty, Siddanna Pallad, DD Deshpande, and SK Shrivastava. "Dosimetric comparison of inverse optimization with geometric optimization in combination with graphical optimization for HDR prostate implants." Journal of Medical Physics 31, no. 2 (2006): 89. http://dx.doi.org/10.4103/0971-6203.26694.

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26

Sharma, Nidhi, Zahid A. Khan, Arshad Noor Siddiquee, and Mohd Atif Wahid. "Multi-response optimization of friction stir welding process parameters for dissimilar joining of Al6101 to pure copper using standard deviation based TOPSIS method." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 18 (2019): 6473–82. http://dx.doi.org/10.1177/0954406219858628.

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Friction stir welding is a new and effective solid-state welding process for joining dissimilar materials such as aluminum (Al) and copper (Cu). Joint quality of the friction stir welded materials gets influenced by the welding strategy and different friction stir welding process parameters, i.e. rotational speed, welding speed, tool design, tool pin offset, and tilt angle. In this paper, the effect of combination of different friction stir welding process parameters during joining of Al-6101 and pure copper is studied using Taguchi L18 orthogonal array. Four friction stir welding process para

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27

Abadou, Yacine, Abderrahmane Ghrieb, Rosa Bustamante, and Hayette Faid. "Optimization by mixture design approach." Journal of Engineering, Design and Technology 18, no. 6 (2020): 1731–47. http://dx.doi.org/10.1108/jedt-11-2019-0311.

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Purpose The purpose of this study is to fit an appropriate mathematical model to express response variables as functions of the proportions of the mixture components. One purpose of statistical modeling in a mixture experiment is to model the blending surface such that predictions of the response for any mixture component, singly or in combination, can be made empirically. Testing of the model adequacy will also be an important part of the statistical procedure. Design/methodology/approach A series of mortar using air lime, marble and ceramic sanitary waste aggregates were prepared for statist

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28

Chen, Wei, Xinxin Wang, Hao Liu, Yang Tang, and Junjie Liu. "Optimized Combination of Spray Painting Trajectory on 3D Entities." Electronics 8, no. 1 (2019): 74. http://dx.doi.org/10.3390/electronics8010074.

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In this research, a novel method of space spraying trajectory optimization is proposed for 3D entity spraying. According to the particularity of the three-dimensional entity, the finite range model is set up, and the 3D entity is patched by the surface modeling method based on FPAG (flat patch adjacency graph). After planning the spray path on each patch, the variance of the paint thickness of the discrete point and the ideal paint thickness is taken as the objective function and the trajectory on each patch is optimized. The improved GA (genetic algorithm), ACO (ant colony optimization), and

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29

Hasan, Doaa Mohamed, Ahmed Sharaf Eldin, Ayman Elsayed Khedr, and Hanan Fahmy. "In-Silico Methodologies for Cancer Multidrug Optimization." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (2018): 7186–205. http://dx.doi.org/10.24297/ijct.v17i2.7168.

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic we

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30

Shi, Shuangshuang, Yanlin Ge, Lingen Chen, and Huijun Feng. "Performance Optimizations with Single-, Bi-, Tri-, and Quadru-Objective for Irreversible Atkinson Cycle with Nonlinear Variation of Working Fluid’s Specific Heat." Energies 14, no. 14 (2021): 4175. http://dx.doi.org/10.3390/en14144175.

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Considering nonlinear variation of working fluid’s specific heat with its temperature, finite-time thermodynamic theory is applied to analyze and optimize the characteristics of an irreversible Atkinson cycle. Through numerical calculations, performance relationships between cycle dimensionless power density versus compression ratio and dimensionless power density versus thermal efficiency are obtained, respectively. When the design parameters take certain specific values, the performance differences of reversible, endoreversible and irreversible Atkinson cycles are compared. The maximum speci

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31

Rivin, Eugene I. "Structural Optimization of Cantilever Mechanical Elements." Journal of Vibration and Acoustics 108, no. 4 (1986): 427–33. http://dx.doi.org/10.1115/1.3269366.

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Naturally limited stiffness of cantilever elements due to lack of constraint from other structural components, together with low structural damping, causes intensive and slow-decaying transient vibrations as well as low stability margins for self-excited vibrations. In cases of dimensional limitations (e.g., boring bars), such common antivibration means as dynamic vibration absorbers have limited effectiveness due to low mass ratios. This paper describes novel concepts of structural optimization of cantilever components by using combinations of rigid and light materials for their design. Two e

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32

Ismail, Mohd Arfian, Vitaliy Mezhuyev, Irfan Darmawan, Shahreen Kasim, Mohd Saberi Mohamad, and Ashraf Osman Ibrahim. "Optimization of Biochemical Systems Production Using Combination of Newton Method and Particle Swarm Optimization." International Journal on Advanced Science, Engineering and Information Technology 9, no. 3 (2019): 753. http://dx.doi.org/10.18517/ijaseit.8.5.4987.

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33

Ismail, Mohd Arfian, Vitaliy Mezhuyev, Irfan Darmawan, Shahreen Kasim, Mohd Saberi Mohamad, and Ashraf Osman Ibrahim. "Optimization of Biochemical Systems Production Using Combination of Newton Method and Particle Swarm Optimization." International Journal on Advanced Science, Engineering and Information Technology 9, no. 3 (2019): 753. http://dx.doi.org/10.18517/ijaseit.9.3.4987.

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34

Kuci, Erin, François Henrotte, Pierre Duysinx, and Christophe Geuzaine. "Combination of topology optimization and Lie derivative-based shape optimization for electro-mechanical design." Structural and Multidisciplinary Optimization 59, no. 5 (2018): 1723–31. http://dx.doi.org/10.1007/s00158-018-2157-2.

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35

Wasnik, Ratnadeep. "Effect of Aggregate Combination on Optimization of Concrete Strength." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 1193–96. http://dx.doi.org/10.22214/ijraset.2020.6194.

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36

SUN, Chuanjiao, Wei ZHOU, and Yuanqing WANG. "Scheduling Combination and Headway Optimization of Bus Rapid Transit." Journal of Transportation Systems Engineering and Information Technology 8, no. 5 (2008): 61–67. http://dx.doi.org/10.1016/s1570-6672(08)60039-2.

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37

Houy, Nicolas, and François Le Grand. "Optimizing treatment combination for lymphoma using an optimization heuristic." Mathematical Biosciences 315 (September 2019): 108227. http://dx.doi.org/10.1016/j.mbs.2019.108227.

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38

Zhan Li and Y. Rahmat-Samii. "Optimization of PIFA-IFA combination in handset antenna designs." IEEE Transactions on Antennas and Propagation 53, no. 5 (2005): 1770–78. http://dx.doi.org/10.1109/tap.2005.846807.

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39

Xiao, Lixia, Pei Xiao, Yue Xiao, et al. "Transmit Antenna Combination Optimization for Generalized Spatial Modulation Systems." IEEE Access 6 (2018): 41866–82. http://dx.doi.org/10.1109/access.2018.2859794.

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40

Figueira de Faria, Luís Filipe, João Eduardo Azevedo Ramos da Silva, and Deisemara Ferreira. "Combination of Techniques: Optimization-Simulation in an Automotive Company." Revista Gestão da Produção Operações e Sistemas 14, no. 4 (2019): 93–113. http://dx.doi.org/10.15675/gepros.v14i4.2300.

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41

Handique, Jyotishman, Sandhan Jyoti Bora, and Nandan Sit. "Optimization of banana juice extraction using combination of enzymes." Journal of Food Science and Technology 56, no. 8 (2019): 3732–43. http://dx.doi.org/10.1007/s13197-019-03845-z.

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Ochoa, Gabriela, and Minaya Villasana. "Population-based optimization of cytostatic/cytotoxic combination cancer chemotherapy." Soft Computing 17, no. 6 (2013): 913–24. http://dx.doi.org/10.1007/s00500-013-1043-5.

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43

Mohd Sabri, Norlina, Nor Diyana Md Sin, Mazidah Puteh, and Mohamad Rusop. "Optimization of RF Magnetron Sputtering Process Parameters Based on Genetic Algorithm and Neural Network." Advanced Materials Research 1109 (June 2015): 481–85. http://dx.doi.org/10.4028/www.scientific.net/amr.1109.481.

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A procedure for RF magnetron sputtering process parameter optimization is proposed in this paper. This study has been focusing on determining the optimal parameter combination for producing the desirable optical band gap. In this proposed procedure, Genetic Algorithm (GA) has been adapted as the optimization tool, while Artificial Neural Network (ANN) has been implemented as the prediction model. GA was adapted to search for the optimal parameter combination from the set of parameters, while later the ANN modeling had been utilized to predict the optical band gap energies for each of the param

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Bora, Kaushik, and Hriday Mani Kalita. "Determination of best groyne combination for mitigating bank erosion." Journal of Hydroinformatics 21, no. 5 (2019): 875–92. http://dx.doi.org/10.2166/hydro.2019.151.

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Abstract This paper presents a novel approach for determining the best combination of groynes in terms of their number, lengths and positions for controlling bank erosion. The vulnerable bank is considered to be protected if a very small value of water flow speed is achieved on the near bank area. A linked simulation–optimization model is developed in this regard which minimizes the total construction cost of the groyne project. At the same time, a constraint in terms of low flow speed in a predefined zone is incorporated, which helps in bank erosion prevention. In the simulation model, the de

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45

Yao, Xiao Ning, Yu Long Zhou, and Xin Sheng Zhang. "Grillage Structural Optimization Based on Genetic Algorithm and Mechanical Direct Calculation." Advanced Materials Research 1061-1062 (December 2014): 1135–39. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.1135.

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In the process of using the genetic algorithm for grillage structural optimization, direct calculation was applied to grillage structural strength Check. The combination of genetic algorithm and direct calculation can save significant time in optimization design and strength check, at the same time, optimized grillage structure provides more security.And then this structure optimization scheme was programmed by VB with easy interface. The example indicates the program, being reasonable and feasible, proves the combination's superiority.

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Hou, Zhi Xiang, Dan Yu, Jia Jia Li, Jian Yang, and Xiao Mei Yin. "Highway Accidents Prediction Model of Combinatorial Optimization." Applied Mechanics and Materials 48-49 (February 2011): 574–78. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.574.

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Although road traffic accidents have contingency characteristics, but also can use the method of forecasting theory to forecast. In this paper, we adopt the gray GM (1,1) model and cubic exponent smooth model to optimum combination ，Established traffic accident prediction model based on IOWGA(induction ordered geometric weighted average)and tested this combination forecasting model .Test results show that the combination forecasting model is effective, reliable, high prediction accuracy, can used to real forecast.

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Du, Dawei, and Dan Simon. "Complex System Optimization Using Biogeography-Based Optimization." Mathematical Problems in Engineering 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/456232.

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Complex systems are frequently found in modern industry. But with their multisubsystems, multiobjectives, and multiconstraints, the optimization of complex systems is extremely hard. In this paper, a new algorithm adapted from biogeography-based optimization (BBO) is introduced for complex system optimization. BBO/Complex is the combination of BBO with a multiobjective ranking system, an innovative migration approach, and effective diversity control. Based on comparisons with three complex system optimization algorithms (multidisciplinary feasible (MDF), individual discipline feasible (IDF), a

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48

Iwatani, Y. "A feedback combination of global optimization and local optimization for robust template-based visual tracking." Advanced Robotics 27, no. 17 (2013): 1351–59. http://dx.doi.org/10.1080/01691864.2013.826415.

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Mahardhika, Taufan. "Hybrid Algorithm as alternative method for optimization, a combination Genetic Algorithm and Particle Swarm Optimization." Journal of Physics: Conference Series 1764, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1742-6596/1764/1/012040.

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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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