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Soft Computing

, Volume 23, Issue 4, pp 1151–1163 | Cite as

Self-feedback differential evolution adapting to fitness landscape characteristics

  • Wei Li
  • Shanni LiEmail author
  • Zhangxin Chen
  • Liang Zhong
  • Chengtian Ouyang
Methodologies and Application

Abstract

Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimization problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distributions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17 benchmark functions, and the experimental results demonstrated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness landscape, then speeds up convergence to the global optimum, and avoids falling into local optima.

Keywords

Differential evolution Self-feedback Fitness landscape Probability distribution Optimization problem 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573157 and 61561024, the Science and Technology Planning Project of Guangdong Province with the Grant No. 2017A010101037, the Science and Technology Research Project of Jiangxi Province under Grant No. GJJ160631, and the Science Foundation of Jiangxi University of Science and Technology under the Grant No. NSFJ2015-K13.

Compliance with ethical standards

Conflicts of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.College of Mathematics and InformaticsSouth China Agricultural UniversityGuangzhouChina
  2. 2.School of Information EngineeringJiangxi University of Science and TechnologyGanzhouChina
  3. 3.Southern Capital Management Co., LtdShenzhenChina
  4. 4.Department of Chemical and Petroleum EngineeringUniversity of CalgaryCalgaryCanada

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