Yin-Yang firefly algorithm based on dimensionally Cauchy mutation

Wen chuan Wang, Lei Xu, Kwok wing Chau, Dong mei Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

84 Citations (Scopus)


Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.

Original languageEnglish
Article number113216
JournalExpert Systems with Applications
Publication statusPublished - 15 Jul 2020


  • Cauchy mutation
  • CEC 2013 benchmark functions
  • Engineering optimization problems
  • GNS strategy
  • Random attraction model
  • Yin-Yang firefly algorithm

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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