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Self-adaptive collective intelligence-based mutation operator for differential evolution algorithms

  • Jinhong FengEmail author
  • Jundong Zhang
  • Chuan Wang
  • Minyi Xu
Article
  • 64 Downloads

Abstract

In conventional differential evolutionary (DE) algorithm, mutation operator has significant influence on generating new vectors by mixing existing target vectors randomly selected from the current population. Recently, many mutation operators, which usually employ the best individual or some high-quality individuals randomly chosen, have been proposed to improve searching capability. However, such designs may easily suffer from premature convergence trapped by local optima. To make a trade-off between exploration and exploitation capability, this paper proposes a novel collective intelligence (CI)-based mutation operator, which is named as “current-to-sa-ci-best.” In the presented mutation operator, the evolutionary information of m best target vectors is linearly combined to generate new mutant vectors. Besides, m is designed as an exponential-distributed random number which could be self-adapted based on successful records of m values alongside evolution. Moreover, this mutation operator could be applied to any DE algorithm without destroying existing search capability by adding a greedy selection operator. To verify its effectiveness, the proposed CI-based mutation strategy, which is named as SaCI, was embedded into some state-of-the-art DE variants on 28 CEC2013 benchmark functions. Numerical results have confirmed that the SaCI operator may be beneficial to DEs to some extent.

Keywords

Differential evolution Mutation Self-adaption Collective intelligence 

Notes

Acknowledgements

The authors would like to thank Natural Science Foundation of China under Contract No. 51709027, 51506019; Natural Science Foundation of Liaoning Province, China under Contract No. 2014025006; Education Department General Project of Liaoning Province, China under Contract No. L2014209; Doctoral Scientific Research Foundation Project of Liaoning Province, China under Contract No. 20170520265; Yong Elite Scientists Sponsorship Program By CAST under Contract No. 2016QNRC001 for financially supporting this research; Fundamental Research Funds for the Central Universities under Contract No. 3132019007.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jinhong Feng
    • 1
    Email author
  • Jundong Zhang
    • 1
  • Chuan Wang
    • 1
  • Minyi Xu
    • 1
  1. 1.Marine Engineering CollegeDalian Maritime UniversityDalianPeople’s Republic of China

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