Notice of RetractionCloning particle swarm optimization with hybrid discrete variables and its application to gear reducer

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.

During the iterative process of standard particle swarm optimization (PSO), the premature convergence of particles decreases the algorithm´s searching ability. Through analyzing the reason of particle premature convergence during the renewal process, introducing the updating strategy based on cloning technique, cloning particle swarm optimization (CPSO) algorithm with hybrid discrete variables model was proposed, and its program CPSO1.0 with Matlab software was developed. The updating strategy based on cloning algorithm makes the particles of cloning particle swarm optimization (CPSO) maintain the diversity during the iterative process, thus overcomes the defect of premature convergence. Example of gear reducer indicates that compared with the exiting algorithms, CPSO gets the best result, thus certify the improvement of the algorithm´s searching ability by cloning mechanism.