Notice of RetractionA hierarchical particle swarm optimization algorithm combined with chaotic search

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.

To overcome disadvantage of Particle Swarm Optimization (PSO) algorithm such as premature convergence and lack of good local search ability, a novel PSO algorithm (HCPSO) is proposed. Based on the hierarchical topology, HCPSO can take a good balance of exploration and exploitation. Combined with chaotic search, HCPSO could explore for better solution around the comprehensive best position whose dimensions learn from the corresponding dimension of other particle´s personal best position. The region of chaotic search is adaptively adjusted according to the distance between the particle´s personal best position and the comprehensive best position. The simulation results of a set of benchmark functions and the comparison with other variants of PSO algorithms verify the efficiency of the HCPSO algorithm.