Advertisement

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
Article

Abstract

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.

Keywords

Swarm intelligence Bionic algorithm Wolf pack Role assignment Distributed system 

Notes

Acknowledgement

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

References

  1. 1.
    Beheshti, Z., Shamsuddin, S.M.: Non-parametric particle swarm optimization for global optimization [J]. Appl. Soft Comput. 28(1), 345–359 (2015)CrossRefGoogle Scholar
  2. 2.
    Sudholt, D.: Theory of swarm intelligence[C]. In: Conference Companion on Genetic and Evolutionary Computation, pp. 1215–1238. ACM (2012)Google Scholar
  3. 3.
    Li, W., Bi, Y., Zhu, X., et al.: Hybrid swarm intelligent parallel algorithm research based on multi-core clusters[J]. Microprocess. Microsyst. 47, 151–160 (2016)CrossRefGoogle Scholar
  4. 4.
    Ma, L., Zhu, Y., Zhang, D., et al.: A hybrid approach to artificial bee colony algorithm[J]. Neural Comput. Appl. 27(2), 387–409 (2016)CrossRefGoogle Scholar
  5. 5.
    Yang, X.S.: Swarm intelligence based algorithms: a critical analysis [J]. Evol. Intel. 7(1), 17–28 (2014)CrossRefGoogle Scholar
  6. 6.
    Yi-Mao, Y.E., Zhao, H.S., Jin, L.: A hybrid optimization algorithm based on particle swarm optimization algorithm and artificial bee colony algorithm [J]. J. Guangxi Univ. Natl. (2013)Google Scholar
  7. 7.
    Mohan, B.C., Baskaran, R.: A survey: Ant Colony Optimization based recent research and implementation on several engineering domain [J]. Expert Syst. Appl. 39(4), 4618–4627 (2012)CrossRefGoogle Scholar
  8. 8.
    Lin, K.C., Chen, S.Y., Hung, J.C.: Feature selection for support vector machines base on modified artificial fish swarm algorithm [M]. In: Ubiquitous Computing Application and Wireless Sensor, pp. 297–304. Springer, Dordrecht (2015)CrossRefGoogle Scholar
  9. 9.
    Muro, C., Escobedo, R., Spector, L., et al.: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations[J]. Behav. Proc. 88(3), 192–197 (2011)CrossRefGoogle Scholar
  10. 10.
    Huan, Zhou, Hui, Zhao, et al.: Cooperative flight and evasion control of UAV swarm based on rule[J]. Syst. Eng. Electron. 38(6), 1374–1382 (2016)Google Scholar
  11. 11.
    Weitzenfeld, A., Vallesa, A., Flores, H.A.: Biologically-inspired wolf pack multiple robot hunting model [C]. In: Lars’06, Robotics Symposium, IEEE, Latin American, pp. 120–127. IEEE (2007)Google Scholar
  12. 12.
    Jun-Hua, L.I., Ming, L.I.: Convergence analysis and convergence rate estimate of cellular genetic algorithms [J]. Pattern Recognit. Artif. Intell. 25(5), 874–878 (2012)Google Scholar
  13. 13.
    Galletly, J.: Evolutionary algorithms in theory and practice [J]. Complexity 2(8), 26–27 (1996)Google Scholar
  14. 14.
    Zhou, X., Gao, D.Y., Yang, C.A.: Comparative study of state transition algorithm with harmony search and artificial bee colony [J]. In: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), vol. 212, pp. 651–659 (2012)Google Scholar
  15. 15.
    Hu, M., Wu, T., Weir, J.D.: An Intelligent Augmentation of Particle Swarm Optimization with Multiple Adaptive Methods [M]. Elsevier Science Inc., New York, (2012)CrossRefGoogle Scholar
  16. 16.
    Motiian, S., Soltanian-Zadeh, H.: Improved particle swarm optimization and applications to Hidden Markov Model and Ackley function [C]. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 1–4. IEEE 2011Google Scholar
  17. 17.
    Tang, Q., Shen, Y., Hu, C., et al.: Swarm intelligence: based cooperation optimization of multi-modal functions [J]. Cognit. Comput. 5(1), 48–55 (2013)CrossRefGoogle Scholar
  18. 18.
    Parpinelli, R.S., Teodoro, F.R., Lopes, H.S.: A comparison of swarm intelligence algorithms for structural engineering optimization[J]. Int. J. Numer. Meth. Eng. 91(6), 666–684 (2012)CrossRefGoogle Scholar
  19. 19.
    Caamaño, P., Bellas, F., Becerra, J.A., et al.: Evolutionary algorithm characterization in real parameter optimization problems[J]. Appl. Soft Comput. 13(4), 1902–1921 (2013)CrossRefGoogle Scholar
  20. 20.
    Wu, J., Jing, Z., Li, R., et al.: A multi-subpopulation PSO immune algorithm and its application on function optimization [J]. Journal of Comput. Res. Dev. 49(9), 1883–1898 (2012)Google Scholar

Copyright information

© 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

Personalised recommendations