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Vol. 159, Issue 11, November 2013, pp. 277-281

 

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An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model
 
Zhehuang Huang

School of Mathematics Sciences, Huaqiao University, Quanzhou, 362021, China Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen, 361005
E-mail: hzh98@hqu.edu.cn

 

Received: 18 September 2013   /Accepted: 25 October 2013   /Published: 30 November 2013

Digital Sensors and Sensor Sysstems

 

Abstract: Differential evolution (DE) algorithm is a good optimization technique based on population which has been successfully applied in many research and application areas. Log-linear model is a statistical model which can easily blend multiple features, a variety of knowledge sources can be added to the model in the form of feature functions. Traditional differential evolution algorithm is easy to fall into local optimum value and the convergence rate is slow. To solve these problems, an improved differential evolution algorithm based on log-linear model is proposed and implemented in this paper. There are two mainly works in this paper. Firstly, we introduce log-linear model to differential evolution algorithm which can enhance decision making ability. Secondly, some operations are presented to improve global optimization capability. Experiments showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

 

Keywords: Differential evolution, Log-linear model, Statistical model, Function optimization.

 

 

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