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Improving differential evolution with a new selection method of parents for mutation

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Abstract

In differential evolution (DE), the salient feature lies in its mutationmechanismthat distinguishes it from other evolutionary algorithms. Generally, for most of the DE algorithms, the parents for mutation are randomly chosen from the current population. Hence, all vectors of population have the equal chance to be selected as parents without selective pressure at all. In this way, the information of population cannot be fully exploited to guide the search. To alleviate this drawback and improve the performance of DE, we present a new selection method of parents that attempts to choose individuals for mutation by utilizing the population information effectively. The proposed method is referred as fitnessand- position based selection (FPS), which combines the fitness and position information of population simultaneously for selecting parents in mutation of DE. In order to evaluate the effectiveness of FPS, FPS is applied to the original DE algorithms, as well as several DE variants, for numerical optimization. Experimental results on a suite of benchmark functions indicate that FPS is able to enhance the performance of most DE algorithms studied. Compared with other selection methods, FPS is also shown to be more effective to utilize information of population for guiding the search of DE.

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Authors and Affiliations

  1. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Yiqiao Cai, Yonghong Chen, Tian Wang & Hui Tian

Authors
  1. Yiqiao Cai
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  2. Yonghong Chen
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  3. Tian Wang
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  4. Hui Tian
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Corresponding author

Correspondence to Yiqiao Cai.

Additional information

Yiqiao Cai is currently a lecturer with the College of Computer Science and Technology at Huaqiao University, China. He received his BS from Hunan University, China in 2007 and PhD from Sun Yat-sen University, China in 2012. His research interests are differential evolution, multiobjective optimization, and other evolutionary computation techniques.

Yonghong Chen is now a professor of College of Computer Science and Technology at Huaqiao University, China. He received his BS from Hubei National University, China, his ME and PhD from Chongqing University, China in 2000 and 2005, respectively. His research interests include network security, watermarking and nonlinear processing.

Tian Wang is currently a lecturer with the College of Computer Science and Technology at Huaqiao University, China. He received his BS and MS in computer science from the Central South University, China in 2004 and 2007, respectively, and PhD in computer science from the City University of Hong Kong, China in 2011. His research interests include wireless networks, Internet of Things, and mobile computing.

Hui Tian is now an associate professor of College of Computer Science and Technology at Huaqiao University, China. He received his BS and MS in 2004 and 2007 fromWuhan Institute of Technology, China, and PhD in 2010 from Huazhong University of Science and Technology, China. His present research interests include network and multimedia information security, digital forensics, and information hiding.

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Cai, Y., Chen, Y., Wang, T. et al. Improving differential evolution with a new selection method of parents for mutation. Front. Comput. Sci. 10, 246–269 (2016). https://doi.org/10.1007/s11704-015-4480-8

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  • DOI: https://doi.org/10.1007/s11704-015-4480-8

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