DocumentCode
1953739
Title
Notice of Retraction
The constrained multi-objective evolutionary algorithm based on the exchange of pairs of groups
Author
Li Hongmei ; Yang Lingen
Author_Institution
Dept. of Comput., Guangdong Baiyun Univ., Guangzhou, China
Volume
3
fYear
2010
fDate
9-11 July 2010
Firstpage
630
Lastpage
633
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 avoid constructing the penalty function and deleting the good infeasible solutions directly, this paper presents the constrained multi-objective optimization evolutionary algorithm based on the exchange of pairs of groups. This algorithm maintains two groups at the same time, one is to save the feasible solutions, the other is to save the infeasible solutions that have some good characteristics. The two groups share a number of excellent features and increase the population diversity by the exchange of information of them. Test results show that the new approach is feasible and effective.
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 avoid constructing the penalty function and deleting the good infeasible solutions directly, this paper presents the constrained multi-objective optimization evolutionary algorithm based on the exchange of pairs of groups. This algorithm maintains two groups at the same time, one is to save the feasible solutions, the other is to save the infeasible solutions that have some good characteristics. The two groups share a number of excellent features and increase the population diversity by the exchange of information of them. Test results show that the new approach is feasible and effective.
Keywords
evolutionary computation; constrained multi-objective evolutionary algorithm; penalty function; population diversity; Optimization; communication strategy; constrained multi-objective optimization; evolutionary algorithm; individual sort;
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.2010.5564825
Filename
5564825
Link To Document