DocumentCode
526433
Title
Notice of Retraction
A improved multi-objective evolutionary algorithm based on Three-way radix quicksort
Author
Guibing Gao ; Guojun Zhang ; Gang Huang ; Peihua Gu ; Fanmao Liu
Author_Institution
Sch. of Mech. Eng., Hubei Univ. of Technol., Wuhan, China
Volume
3
fYear
2010
fDate
9-11 July 2010
Firstpage
378
Lastpage
382
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.
A improved multi-objective evolutionary algorithm based on Three-way radix quicksort (TQIEA ) is presented in this paper for multi-objective optimization problems(MOPs). This algorithm uses the idea of three-way radix quicksort to divided the population into three sections, Recursively sort the different sections until all the individuals have been classified and assigned the fitness value. The proposed algorithm is validated by 6 benchmark test problems. Compared with four other state-of-the-art multi-objective algorithms, TQIEA achieves competitive results in two quality indicators.
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.
A improved multi-objective evolutionary algorithm based on Three-way radix quicksort (TQIEA ) is presented in this paper for multi-objective optimization problems(MOPs). This algorithm uses the idea of three-way radix quicksort to divided the population into three sections, Recursively sort the different sections until all the individuals have been classified and assigned the fitness value. The proposed algorithm is validated by 6 benchmark test problems. Compared with four other state-of-the-art multi-objective algorithms, TQIEA achieves competitive results in two quality indicators.
Keywords
evolutionary computation; optimisation; sorting; benchmark test problem; fitness value assignement; improved multiobjective evolutionary algorithm; multiobjective optimization; population division; three-way radix quicksort; Computers; Three-way radix quicksort; evolutionary algorithm; multi-objective optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564021
Filename
5564021
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