Notice of RetractionAn improved particle swarm optimization algorithm for solving dynamic tugboat scheduling problem

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.