Notice of RetractionOptimization of Fuzzy Controller Based on Dynamic Evolutionary Algorithm

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 performance of fuzzy control is depends on the fuzzy control rules, quantification factor and the scale factor, because of these multiple objectives are influenced with each other. Therefore, it is very difficult to ensure the control effect only rely on expertise´s experience. In this paper, we propose a dynamic evolutionary algorithm to optimize the fuzzy controller´s multi-objectives based on the competitive relationship between particles´ free energy and entropy in the phase space system. A new option strategy is put forward to maintain species diversity, and multi-parent crossover operator select some individuals to form a space and then do searching in this space. The algorithm has strong ability to find the solution of the problem, and it also faster compared with other traditional algorithms. Simulation results show that fuzzy controller optimized by our algorithm has good steady-state response time, steady-state error and overshoot.