Improved cluster collaboration algorithm based on wolf pack behavior
- 180 Downloads
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.
KeywordsSwarm intelligence Bionic algorithm Wolf pack Role assignment Distributed system
The research is supported by National Aviation Foundation of China No. 2016ZC15012.
- 2.Sudholt, D.: Theory of swarm intelligence[C]. In: Conference Companion on Genetic and Evolutionary Computation, pp. 1215–1238. ACM (2012)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)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)Google Scholar
- 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)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 2011Google 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