DocumentCode
1971043
Title
Notice of Retraction
Optimization of Fuzzy Controller Based on Dynamic Evolutionary Algorithm
Author
Zeng Hui ; Li Yuanxiang ; Xia Xuewen
Author_Institution
Sch. of Comput. Sci., Wuhan Univ., Wuhan, China
fYear
2010
fDate
22-23 June 2010
Firstpage
400
Lastpage
403
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 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.
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.
Keywords
entropy; evolutionary computation; free energy; fuzzy control; fuzzy set theory; optimisation; phase space methods; search problems; entropy; evolutionary algorithm; free energy; fuzzy control; multiparent crossover operator; optimization; overshoot; phase space system; quantification factor; scale factor; search algorithm; search problem; steady-state error; steady-state response time; Evolutionary computation; Fuzzy control; Heuristic algorithms; Mathematical model; Optimization; Process control; Quantization; dynamic evolutionary algorithm; fuzzy control; multi-parent crossover; multiobjectives optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on
Conference_Location
Kuala Lumpur
Print_ISBN
978-1-4244-6640-5
Type
conf
DOI
10.1109/ICICCI.2010.61
Filename
5565948
Link To Document