期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 150, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113216
关键词
Yin-Yang firefly algorithm; Cauchy mutation; GNS strategy; Random attraction model; CEC 2013 benchmark functions; Engineering optimization problems
类别
资金
- Henan province university scientific and technological innovation team [18IRT-STHN009]
- National Natural Science Foundation of China [51509088]
- Henan Key Laboratory of Water Environment Simulation and Treatment [2017016]
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. (C) 2020 Elsevier Ltd. All rights reserved.
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