DocumentCode
2666450
Title
Notice of Retraction
Differential Evolution based on improved mutation strategy
Author
Zhijun Liu ; Yanqi Zhang ; Zhengang Ning ; Yanfeng Zhang ; Yong Guan
Author_Institution
Dept. of Comput. & Inf. Eng., Mechanic & Electron. Technol. Inst. of Hebei, Xingtai, China
Volume
4
fYear
2010
fDate
16-18 April 2010
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.
Differential Evolution (DE) is well-known as a simple and efficient scheme for global optimization over continuous spaces. However, the mutation strategies used in DE greatly affect its performance. In this paper, we present a novel DE variant with an improved mutation strategy. The proposed approach, called IDE, is motivated by the idea of Particle Swarm Optimization (PSO), in which each individual flies to better positions by learning its previous best and other best individuals´ experiences. In order to verify the performance of IDE, we test it on ten famous benchmark functions. The simulation results show that IDE outperforms classical DE, self-adaptive DE (SaDE), and DE with neighborhood search (NSDE) on the majority of test problems.
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.
Differential Evolution (DE) is well-known as a simple and efficient scheme for global optimization over continuous spaces. However, the mutation strategies used in DE greatly affect its performance. In this paper, we present a novel DE variant with an improved mutation strategy. The proposed approach, called IDE, is motivated by the idea of Particle Swarm Optimization (PSO), in which each individual flies to better positions by learning its previous best and other best individuals´ experiences. In order to verify the performance of IDE, we test it on ten famous benchmark functions. The simulation results show that IDE outperforms classical DE, self-adaptive DE (SaDE), and DE with neighborhood search (NSDE) on the majority of test problems.
Keywords
evolutionary computation; particle swarm optimisation; IDE; PSO; continuous spaces; global optimization; improved differential evolution; improved mutation strategy; individual flies; particle swarm optimization; Automatic testing; Benchmark testing; Chromium; Electronic design automation and methodology; Evolutionary computation; Genetic mutations; Particle swarm optimization; Random number generation; Signal processing algorithms; Sun; differential evolution (DE); evolutionary algorithm; mutation; optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486355
Filename
5486355
Link To Document