A Novel Ant Colony Optimization Algorithm with Dynamic Control Population for Community Detecting

  • Jianjun Chen
  • Shupeng Gao
  • Zhen Su
  • Siqi Chen
  • Xianghua LiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


Nowadays, complex networks have driven great interests of scholars. As a special characteristic of a network, the community structure has wide research prospects. Many current algorithms are adopted for detecting the potential community structure, in which the ant colony algorithm is a typical one. However, the computational cost of the ant colony is too high which limits its performance. In this paper, we propose a novel ant colony optimization algorithm with dynamic control population. In the proposed algorithm, when a certain condition is reached, the number of ants starts to decrease based on the proposed rules. The efficiency of the proposed algorithm is estimated through comparing with the classical ant colony algorithm in real-world networks. Experiments show that the proposed algorithm has apparently lower computational cost, while the quality of the division is reserved relatively.


Community mining Ant colony optimization Dynamic control population 



This work was supported by National Natural Science Foundation of China (No. 61602391), Natural Science Foundation of Chongqing (No. cstc2018jcyjAX0274), and in part by the National Training Programs of Innovation and Entrepreneurship for Undergraduates.


  1. 1.
    Newman, M.E.J.: The structure and function of complex networks. Soc. Ind. Appl. Math. 45(2), 167–256 (2003)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Weng, L.L., Menczer, F., Ahn, Y.Y.: Virality prediction and community structure in social networks. Sci. Rep. 3, 2522 (2013)CrossRefGoogle Scholar
  4. 4.
    Karrer, B., Newman, M.E.J.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ruan, J.H., Zhang, W.X.: An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In: 7th IEEE International Conference on Data Mining, pp. 643–648 (2007)Google Scholar
  6. 6.
    Nakagaki, T., Yamada, H., Tóth, Á.: Intelligence: maze-solving by an amoeboid organism. Nature 407(6803), 470–470 (2000)CrossRefGoogle Scholar
  7. 7.
    Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for biologically inspired adaptive network design. Science 327(5964), 439–442 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gong, M.G., Cai, Q., Chen, X.W., Ma, L.J.: Complex network clustering by multi objective discrete particle swarm optimization based on decomposition. IEEE Trans. Evol. Comput. 18(1), 82–97 (2014)CrossRefGoogle Scholar
  9. 9.
    Mandala, S.R., Kumara, S.R., Rao, C.R., Albert, R.: Clustering social networks using ant colony optimization. Oper. Res. Int. J. 13(1), 47–65 (2013)CrossRefGoogle Scholar
  10. 10.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  11. 11.
    Gao, C., Liang, M.X., Li, X.H., Zhang, Z.L., Wang, Z., Zhou, Z.L.: Network community detection based on the Physarum-inspired computational framework. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(6), 1916–1928 (2018)CrossRefGoogle Scholar
  12. 12.
    Mohan, B.C., Baskaran, R.: A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst. Appl. 39(4), 4618–4627 (2012)CrossRefGoogle Scholar
  13. 13.
    Liu, Y.X., Gao, C., Zhang, Z.L., Lu, Y.Q., Chen, S., Liang, M.X., Tao, L.: Solving NP-hard problems with physarum-based ant colony system. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 108–120 (2017)CrossRefGoogle Scholar
  14. 14.
    Jin, D., Liu, D.Y., Yang, B., Liu, J., He, D.X.: Ant colony optimization with a new random walk model for community detection in complex networks. Adv. Complex Syst. 14(05), 795–815 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509(2), 187–195 (2004)CrossRefGoogle Scholar
  16. 16.
    Mu, C.H., Zhang, J., Jiao, L.C.: An intelligent ant colony optimization for community detection in complex networks. In: 2014 IEEE Congress on Evolutionary Computation, pp. 700–706 (2014)Google Scholar
  17. 17.
    Gao, C., Chen, S., Li, X.H., Huang, J.J., Zhang, Z.L.: A physarum-inspired optimization algorithm for load-shedding problem. Appl. Soft Comput. 61, 239–255 (2017)CrossRefGoogle Scholar
  18. 18.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  19. 19.
    Jin, D., Chen, Z., He, D.X., Zhang, W.X.: Modeling with node degree preservation can accurately find communities. In: The 29th AAAI Conference on Artificial Intelligence, pp. 160–167 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jianjun Chen
    • 1
  • Shupeng Gao
    • 2
  • Zhen Su
    • 1
  • Siqi Chen
    • 3
  • Xianghua Li
    • 1
    Email author
  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.School of Mechanical EngineeringNorthwestern Polytechnical UniversityShanxiChina
  3. 3.College of Intelligence and ComputingTianjin UniversityTianjinChina

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