DocumentCode
532414
Title
Notice of Retraction
Study on shape optimization of transmission tower based on genetic algorithm
Author
Fenglin Gan ; XueNing Zhou
Author_Institution
Coll. of Civil Eng., Northeast Dianli Univ., Jilin, China
Volume
6
fYear
2010
fDate
22-24 Oct. 2010
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.
The transmission tower shape can be optimized by genetic algorithm to acquire the maximum fundamental frequency, Considering that there are some disadvantages such as premature convergence and low robustness in solving complex optimization problems with standard genetic algorithm, a new adaptive strategy is proposed to improve the performance of the algorithm, including the design for selection mechanism and the method of selecting both dynamic crossover and mutation probability. Then a solution is put forward for several problems in independent optimization program. Finally, a tower with 108 bars is designed to demonstrate the feasibility and effectiveness of the method.
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.
The transmission tower shape can be optimized by genetic algorithm to acquire the maximum fundamental frequency, Considering that there are some disadvantages such as premature convergence and low robustness in solving complex optimization problems with standard genetic algorithm, a new adaptive strategy is proposed to improve the performance of the algorithm, including the design for selection mechanism and the method of selecting both dynamic crossover and mutation probability. Then a solution is put forward for several problems in independent optimization program. Finally, a tower with 108 bars is designed to demonstrate the feasibility and effectiveness of the method.
Keywords
genetic algorithms; poles and towers; probability; dynamic crossover; genetic algorithm; independent optimization program; mutation probability; shape optimization; transmission tower; Argon; Loading; Optimization; Genetic Algorithm; Shape Optimization; Transmission Tower;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Type
conf
DOI
10.1109/ICCASM.2010.5620487
Filename
5620487
Link To Document