DocumentCode
1959962
Title
Notice of Retraction
Application research of vehicle routing problem based on an improved ant colony algorithm
Author
Sun Yunshan ; Zhang Liyi ; Duan Jizhong
Author_Institution
Coll. of Inf. Eng., Tianjin Univ. of Commerce, Tianjin, China
Volume
7
fYear
2010
fDate
9-11 July 2010
Firstpage
468
Lastpage
472
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.
An improved and colony algorithm was proposed. Genetic algorithm was utilized to optimize the parameters of ant colony algorithm. The improved algorithm was used to solve the optimization routing of the basic vehicle routing problem. The algorithm possesses some characteristics such as strong total researching ability. The experimental results show that the improved ant colony algorithm possesses better optimization quantity and effect than the traditional ant colony algorithm.
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.
An improved and colony algorithm was proposed. Genetic algorithm was utilized to optimize the parameters of ant colony algorithm. The improved algorithm was used to solve the optimization routing of the basic vehicle routing problem. The algorithm possesses some characteristics such as strong total researching ability. The experimental results show that the improved ant colony algorithm possesses better optimization quantity and effect than the traditional ant colony algorithm.
Keywords
combinatorial mathematics; genetic algorithms; genetic algorithm; improved ant colony algorithm; optimization routing; vehicle routing problem; Algorithm design and analysis; Biological system modeling; Computational modeling; Indexes; Phase change materials; ant colony algorithm; genetic algorithm; routing optimization; vehicle routing problem;
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.2010.5565134
Filename
5565134
Link To Document