DocumentCode
527536
Title
Notice of Retraction
Optimization for Giant magnetostrictive smart component based on multi-objective genetic algorithm
Author
Xiao-Mei Sui ; Zhang-Rong Zhao ; Xu-Ming Wang ; Xia-Jun Meng
Author_Institution
Dept. of Electr. Inf., North China Inst. of Sci. & Technol., Beijing, China
Volume
1
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
466
Lastpage
470
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to machine the non-cylinder piston pinhole, a new method is proposed by applying the Giant magnetostrictive materials (GMM) component. An optimization design model combining the smart component genetic algorithm with the finite element method for GMM smart component is established. Nondominated sorting genetic algorithm (NSGA) is used to optimize the model. The optimum results show that the NSGA combining with finite element method is a good way to carry out the optimization design of GMM smart component.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to machine the non-cylinder piston pinhole, a new method is proposed by applying the Giant magnetostrictive materials (GMM) component. An optimization design model combining the smart component genetic algorithm with the finite element method for GMM smart component is established. Nondominated sorting genetic algorithm (NSGA) is used to optimize the model. The optimum results show that the NSGA combining with finite element method is a good way to carry out the optimization design of GMM smart component.
Keywords
design engineering; electromagnetic actuators; finite element analysis; genetic algorithms; intelligent actuators; magnetostrictive devices; FEM; GMM smart component; finite element method; giant magnetostrictive materials component; multi-objective genetic algorithm; noncylinder piston pinhole; nondominated sorting genetic algorithm; optimization design model; smart component genetic algorithm; Coils; Magnetic circuits; Magnetic resonance; Magnetic separation; Magnetostriction; Optimization; FEM; Multi-objective genetic algorithm; Non-dominated sorting genetic algorithm; giant magnetostrictive material (GMM); optimum design; smart component;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5583153
Filename
5583153
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