DocumentCode
512518
Title
Notice of Retraction
An improved particle swarm optimization algorithm for solving dynamic tugboat scheduling problem
Author
Wei Yan ; Zhicheng Bian ; Daofang Chang ; Youfang Huang
Author_Institution
Eng. Res. Center of Container Supply Chain Technol., Shanghai Maritime Univ., Shanghai, China
Volume
1
fYear
2009
fDate
19-20 Dec. 2009
Firstpage
433
Lastpage
437
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 dynamic tugboat scheduling problem is a special kind of Job Scheduling Problem. Here we propose an improved particle swarm optimization (PSO) algorithm in which an entropy function and an elite set is used for better performance. When the entropy of the swarm keeps under some value in a certain period, some of the particles will be replaced randomly by those from the elite set. This may help the swarm to get rid of local optima, and to have a faster convergence. The proposed algorithm is successfully used in the tugboats scheduling problem, and demonstrated its advantages of faster convergence and better result than the standard PSO.
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 dynamic tugboat scheduling problem is a special kind of Job Scheduling Problem. Here we propose an improved particle swarm optimization (PSO) algorithm in which an entropy function and an elite set is used for better performance. When the entropy of the swarm keeps under some value in a certain period, some of the particles will be replaced randomly by those from the elite set. This may help the swarm to get rid of local optima, and to have a faster convergence. The proposed algorithm is successfully used in the tugboats scheduling problem, and demonstrated its advantages of faster convergence and better result than the standard PSO.
Keywords
boats; convergence; entropy; particle swarm optimisation; scheduling; convergence; dynamic tugboat scheduling problem; entropy function; job scheduling problem; particle swarm optimization; Constraint optimization; Convergence; Dynamic scheduling; Entropy; Heuristic algorithms; Intelligent transportation systems; Job shop scheduling; Particle swarm optimization; Scheduling algorithm; Single machine scheduling; PSO; dynamic; scheduling; tug;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on
Conference_Location
Shenzhen
Print_ISBN
978-1-4244-4544-8
Type
conf
DOI
10.1109/PEITS.2009.5406976
Filename
5406976
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