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Improved cluster collaboration algorithm based on wolf pack behavior

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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.

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References

  1. Beheshti, Z., Shamsuddin, S.M.: Non-parametric particle swarm optimization for global optimization [J]. Appl. Soft Comput. 28(1), 345–359 (2015)

    Article  Google Scholar 

  2. Sudholt, D.: Theory of swarm intelligence[C]. In: Conference Companion on Genetic and Evolutionary Computation, pp. 1215–1238. ACM (2012)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  5. Yang, X.S.: Swarm intelligence based algorithms: a critical analysis [J]. Evol. Intel. 7(1), 17–28 (2014)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

  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. Galletly, J.: Evolutionary algorithms in theory and practice [J]. Complexity 2(8), 26–27 (1996)

    Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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 2011

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

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Acknowledgement

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

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710129, China

    Weihao Liang, Jianhua He & Shixiong Wang

  2. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Lei Yang

  3. Department of System Design, China Aeronautical Radio Electronics Research Institute, Shanghai, 200233, China

    Fang Chen

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  1. Weihao Liang
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  2. Jianhua He
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  3. Shixiong Wang
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  4. Lei Yang
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  5. Fang Chen
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Correspondence to Weihao Liang or Jianhua He.

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Liang, W., He, J., Wang, S. et al. Improved cluster collaboration algorithm based on wolf pack behavior. Cluster Comput 22 (Suppl 3), 6181–6196 (2019). https://doi.org/10.1007/s10586-018-1891-y

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