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Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

Qian Zhang, Mahdi Mahfouf, John R. Yates, Christophe Pinna, George Panoutsos, Soufiene Boumaiza, Richard J. Greene, Luis de Leon
2011 Materials and Manufacturing Processes  
Based on the developed reliable prediction models, NSGA-II is further applied to the multiobjective optimal design of aluminium alloys in a 'reverse-engineering' fashion.  ...  The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model's structure while  ...  They also wish to acknowledge the financial support for this work from the European Union under the Framework 6 initiative.  ... 
doi:10.1080/10426914.2010.537421 fatcat:shjwh33m65acfadqsmvo7zfesm

Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

Amin Moslemi Petrudi, Pourya Fathi, Masoud Rahmani
2020 Journal of Modeling and Simulation of Materials  
NSGA II algorithm was used to maximize Nusselt number and minimum friction coefficient by changing temperature and volume fraction of nanoparticles.  ...  In order to evaluate the objective functions in the optimization, the response surface methodology is attached to the optimization algorithm.  ...  . • with the help of this method, optimum quasi -optimal responses are determined. • The second version of the NSGA algorithm, called NSGA II, was introduced due to the relatively high sensitivity that  ... 
doi:10.21467/jmsm.3.1.1-14 fatcat:ytonanzaunconbmb6owurwkwqe

Investigations and optimization for hard milling process parameters using hybrid method of RSM and NSGA-II

2016 Revista Técnica de la Facultad de Ingeniería Universidad del Zulia  
In order to seek optimal parameters, a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted and a set of Pareto-optimal solution set was obtained.  ...  Also, the results would be more useful to guide the actual hard milling process parameters for predicting the responses.  ...  Non-dominated Sorting Genetic Algorithm-II A multi-objective evolutionary algorithm of NSGA-II was developed by Deb K et al., (2002).  ... 
doi:10.21311/ fatcat:pwcheuacvvfgbnferzjon5mfuy

RSM and BPNN Modeling in Incremental Sheet Forming Process for AA5052 Sheet: Multi-Objective Optimization Using Genetic Algorithm

Xiao Xiao, Jin-Jae Kim, Myoung-Pyo Hong, Sen Yang, Young-Suk Kim
2020 Metals  
The optimized Pareto front produced by the GA can be a rational design guide for practical applications of AA5052 in the ISF process.  ...  In this study, the response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the forming parameters of  ...  Process of the non-dominated sorting genetic algorithm II (NSGA-II) algorithm. Figure 16 . 16 Figure 16. Response surface method (RSM)-NSGA-II algorithm for multi-objective optimization.  ... 
doi:10.3390/met10081003 fatcat:tb36cynbgnaxzpsgklxiagh5ii

Optimizing genetic algorithm parameters for multiple sequence alignment based on structural information

May Rashiele K. Sueno, Joel M. Addawe
2016 Advanced Studies in Biology  
MSA is generally the alignment of three or more protein or nucleic acid sequences that maximises the similarities between sequences.  ...  Multiple sequence alignments (MSAs) are commonly used approaches in the analysis of sequence structure relationships.  ...  The authors would like to thank the University of the Philippines for the support.  ... 
doi:10.12988/asb.2016.51250 fatcat:l2ypk5o44jh5ziapemf3vf2i3i

A Decomposition and Dominance-Based Multiobjective Artificial Bee Colony Algorithm for Multiple Sequence Alignment

Lei Ye, Jianhui Lv
2022 Mobile Information Systems  
The scout bee strategy facilitates the algorithm to get out of the local optimal. A comparative experiment is implemented on BAliBASE 3.0, a benchmark for MSA algorithms.  ...  This study proposes a decomposition and dominance-based multiobjective artificial bee colony optimization algorithm for MSA (MOABC/D-MSA).  ...  all observed residue-residue contacts and f i and f j are the single residue frequencies in the dataset considered.  ... 
doi:10.1155/2022/5444055 fatcat:2pmklqflsfe37gld3f4h6g3lta

Optimization of Multi-Track Laser-Cladding Process of Titanium Alloy Based on RSM and NSGA-II Algorithm

Linsen Shu, Jiahao Li, Han Wu, Zhao Heng
2022 Coatings  
In order to obtain the best process parameters of laser-cladding TC4 alloy powder, a method of laser-cladding parameters' optimization based on the RSM and NSGA-II Algorithm is proposed.  ...  The second generation non-dominant sorting genetic algorithm (NSGA-II) was used to optimize the process parameters and the optimization results were verified by experiments.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/coatings12091301 fatcat:nogwdgw55bdedbxcmz3gqvcomq

Predictive prosthetic socket design: part 2—generating person-specific candidate designs using multi-objective genetic algorithms

J. W. Steer, P. A. Grudniewski, M. Browne, P. R. Worsley, A. J. Sobey, A. S. Dickinson
2019 Biomechanics and Modeling in Mechanobiology  
Importantly, insights gained about the design should be seen as a compliment, not a replacement, for the prosthetist's skill and experience.  ...  The result of the analysis is a series of prosthetic socket designs that preferentially load and unload the pressure tolerant and intolerant regions of the residual limb.  ...  ): NSGA-II and cMLSGA.  ... 
doi:10.1007/s10237-019-01258-7 pmid:31741116 fatcat:dqponpcez5gqnecxzg3pocp73q

A Multi-Objective Approach for Protein Structure Prediction Based on an Energy Model and Backbone Angle Preferences

Jyh-Jong Tsay, Shih-Chieh Su, Chin-Sheng Yu
2015 International Journal of Molecular Sciences  
Protein structure prediction (PSP) is concerned with the prediction of protein tertiary structure from primary structure and is a challenging calculation problem.  ...  This study presents a novel backbone angle preference factor, which is one of the factors inducing protein folding.  ...  Acknowledgments We would like thank Roy Preece at Oxford Brookes University for the proofreading of the manuscript.  ... 
doi:10.3390/ijms160715136 pmid:26151847 pmcid:PMC4519891 fatcat:crnv6g5unvhrzo5h5a5usy6xxi

NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms

Makram Soui, Nesrine Mansouri, Raed Alhamad, Marouane Kessentini, Khaled Ghedira
2021 Nonlinear dynamics  
The obtained results prove the efficiency of NSGA-II as a feature selection algorithm combined with AdaBoost classifier.  ...  In this paper, we use the non-dominated sorting genetic algorithm (NSGA-II) to select the interesting features by finding the best trade-offs between two conflicting objectives: minimizing the number of  ...  Acknowledgements The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 7848  ... 
doi:10.1007/s11071-021-06504-1 pmid:34025034 pmcid:PMC8129611 fatcat:nwohijaan5bpfdr2w7izl3lduy

Structural Strength and Reliability Analysis of Important Parts of Marine Diesel Engine Turbocharger

Lin Lei, Ming-ze Ding, Hong-wei Hu, Yun-xiao Gao, Hai-lin Xiong, Wei Wang, Weichao SHI
2021 Mathematical Problems in Engineering  
Therefore, a multiobjective optimization method based on the NSGA-II genetic algorithm is proposed to obtain the multiobjective optimization scheme data with the reliability and processing cost of turbine  ...  At the same time, it is verified by simulation, the data based on NSGA-II multiobjective genetic algorithm are more accurate and have practical engineering reference value.  ...  Acknowledgments is study was supported by the development of a marine low-speed turbocharger (CDGCO1-KT0608), a high-tech marine scientific research project of the Ministry of Industry and Information  ... 
doi:10.1155/2021/5547762 fatcat:aipmvhhv75dpna2qla35xee6fm

Design of Broaching Tool Using Finite Element Method for Achieving the Lowest Residual Tensile Stress in Machining of Ti6Al4V Alloy

2020 International Journal of Engineering  
For the second tooth, a multi-objective genetic algorithm has been used.  ...  Ultimately, the geometry of the broaching tool utility has been designed to store the lowest tensile residual stresses in the machined surface for Ti6Al4V alloy.  ...  Optimization of Geometry of the First Tooth In this research, for the purpose of optimization, the Nondominated Sorting Genetic Algorithm (NSGA-II), has been used.  ... 
doi:10.5829/ije.2020.33.04a.17 fatcat:7nwtvw456ncqlbnnn4da2pu5j4

Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation Process

Mohammad Reza Zaker, Clémence Fauteux-Lefebvre, Jules Thibault
2021 Processes  
MOO studies were performed for various design scenarios involving a variable number of catalytic beds and different reactor configurations.  ...  In this study, a representative kinetic rate equation was rigorously selected to develop a mathematical model to perform the multi-objective optimization (MOO) of the reactor.  ...  In this investigation, the Non-Sorting Genetic Algorithm II (NSGA II) has been used [22] .  ... 
doi:10.3390/pr9061072 fatcat:73dgqxrasbhipjqdeclsvqft2q

Multi-objective optimization of glass multi-station bending machining for smartphone curved screen

Wenbin He, Zhijun Chen, Wuyi Ming, Jinguang Du, Yang Cao, Jun Ma, Aiyun Wei
2019 Journal of the Brazilian Society of Mechanical Sciences and Engineering  
Furthermore, a multi-objective optimization method based on NSGA-III (a non-dominant sorting genetic algorithms based on reference points) was applied to efficiently solve the optimization problem between  ...  Glass multi-station bending machining (GMBM) is a high-precision and efficient glass processing technique for smartphone curved screen in 3C industry.  ...  Multi-objective optimization by NSGA-III NSGA-III is an improved algorithm based on NSGA-II.  ... 
doi:10.1007/s40430-019-1985-3 fatcat:yh4ncii4svbcxfrcqiyaezw6ra

Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering [chapter]

Antonio López Jaimes, Carlos A. Coello Coello
2008 Multi-Objective Optimization  
In this chapter, we provide a general overview of evolutionary multi-objective optimization, with a particular emphasis on the algorithms in current use.  ...  In the final part of the chapter, some potential areas for future research in this area are briefly described. keywords: evolutionary multi-objective optimization, chemical engineering, metaheuristics,  ...  Acknowledgements The second author acknowledges support from CONACyT project no. 45683-Y.  ... 
doi:10.1142/9789812836526_0003 fatcat:bygib2gmvveofgmdcqxvj63r4q
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