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

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. Copyright © 2013 IFSA.