Cluster Computing

, Volume 22, Supplement 3, pp 6181–6196 | Cite as

Improved cluster collaboration algorithm based on wolf pack behavior

  • Weihao LiangEmail author
  • Jianhua HeEmail author
  • Shixiong Wang
  • Lei Yang
  • Fang Chen


Swarm intelligence inspired algorithms have so many profound natural advantages in solving large-scale and distributed problems. This paper systematically analyzes the characteristics of wolves’ behaviors such as cooperative searching, hunting and attacking, and further abstracts those behaviors into four basic ways, that is, wandering, summoning, lurking and besieging, in accordance with the different roles of wolves. Then, we formulate a cluster cooperative rule based on the principle of Dynamic Wolf Head Alternation and Real-time Role Assignment, and propose a fatigue-rendering tactics based on interception strategy in two teams. Finally, the clustering cooperative rule enlightened by the group’s behavior is established, and the convergence of the algorithm is proved with the Markov asymptotic convergence theory. Experiments show that the model can effectively guarantee the efficiency of solving large-scale complex optimization problems and the operational effectiveness of distributed cluster cooperative attack problems.


Swarm intelligence Bionic algorithm Wolf pack Role assignment Distributed system 



The research is supported by National Aviation Foundation of China No. 2016ZC15012.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronics and InformationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Department of System DesignChina Aeronautical Radio Electronics Research InstituteShanghaiChina

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