Soft Computing

, Volume 23, Issue 9, pp 3113–3128 | Cite as

Self-adaptive parameters in differential evolution based on fitness performance with a perturbation strategy

  • Chen-Yang ChengEmail author
  • Shu-Fen Li
  • Yu-Cheng Lin
Methodologies and Application


Differential evolution (DE) algorithms have been used widely to solve optimization problems and practical cases and have demonstrated high efficiency, performing favorably using only a few parameters. Compared with other traditional algorithms, DE algorithms perform well when used to solve continuous problems. To obtain an approximate solution using DE, it is critical that appropriate parameter values are selected. However, selecting and dynamically tuning the parameter values during evolution are not easy tasks because the values depend significantly on the problem to be solved. To address these issues, this study presents an enhanced DE algorithm with self-adaptive adjustable parameters and a perturbation strategy based on individual fitness performance. Compared with two existing DE algorithms, the proposed algorithm can solve six benchmark functions and has both high efficiency and stability.


Self-adaptive parameters Differential evolution Perturbation strategy Parameter adjusting Fitness performance 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Industrial Engineering and ManagementNational Taipei University of TechnologyTaipeiTaiwan
  2. 2.Department of Industrial Engineering and Enterprise InformationTunghai UniversityTaichungTaiwan

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