Soft Computing

, Volume 21, Issue 18, pp 5325–5339 | Cite as

Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

  • Hui Wang
  • Zhihua Cui
  • Hui Sun
  • Shahryar Rahnamayan
  • Xin-She Yang
Methodologies and Application

Abstract

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants.

Keywords

Firefly algorithm (FA) Random attraction Neighborhood search Dynamic parameter adjustment mechanism Global optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Information EngineeringNanchang Institute of TechnologyNanchangChina
  2. 2.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent ProcessingNanchangChina
  3. 3.School of Computer Science and TechnologyTaiyuan University of Science and TechnologyTaiyuanChina
  4. 4.Department of Electrical, Computer, and Software EngineeringUniversity of Ontario Institute of Technology (UOIT)OshawaCanada
  5. 5.School of Science and TechnologyMiddlesex UniversityLondonUK

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