Modelling the Social Interactions in Ant Colony Optimization

  • Nishant Gurrapadi
  • Lydia Taw
  • Mariana MacedoEmail author
  • Marcos Oliveira
  • Diego Pinheiro
  • Carmelo Bastos-Filho
  • Ronaldo Menezes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


Ant Colony Optimization (ACO) is a swarm-based algorithm inspired by the foraging behavior of ants. Despite its success, the efficiency of ACO has depended on the appropriate choice of parameters, requiring deep knowledge of the algorithm. A true understanding of ACO is linked to the (social) interactions between the agents given that it is through the interactions that the ants are able to explore-exploit the search space. We propose to study the social interactions that take place as artificial agents explore the search space and communicate using stigmergy. We argue that this study bring insights to the way ACO works. The interaction network that we model out of the social interactions reveals nuances of the algorithm that are otherwise hard to notice. Examples include the ability to see whether certain agents are more influential than others, the structure of communication, to name a few. We argue that our interaction-network approach may lead to a unified way of seeing swarm systems and in the case of ACO, remove part of the reliance on experts for parameter choice.


Swarm intelligence Swarm-based algorithms Ant colony optimization Interaction network Social interactions 



The authors acknowledge support from National Science Foundation (NSF) grant No. 1560345 ( Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. This work also used the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges at the Pittsburgh Supercomputing Center through allocation TG-IRI180008, which is supported by National Science Foundation grant number ACI-1548562 [11].


  1. 1.
    Bratton, D., Blackwell, T.: Understanding particle swarms through simplification: a study of recombinant PSO. In: Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 2621–2628. ACM (2007)Google Scholar
  2. 2.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)CrossRefGoogle Scholar
  3. 3.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)Google Scholar
  4. 4.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2006)Google Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  6. 6.
    Krömer, P., Gajdo, P., Zelinka, I.: Towards a network interpretation of agent interaction in ant colony optimization. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1126–1132. IEEE (2015)Google Scholar
  7. 7.
    Oliveira, M., Bastos-Filho, C.J., Menezes, R.: Towards a network-based approach to analyze particle swarm optimizers. In: 2014 IEEE Symposium on Swarm Intelligence (SIS), pp. 1–8. IEEE (2014)Google Scholar
  8. 8.
    Oliveira, M., Pinheiro, D., Andrade, B., Bastos-Filho, C., Menezes, R.: Communication diversity in particle swarm optimizers. In: Dorigo, M., Birattari, M., Li, X., López-Ibáñez, M., Ohkura, K., Pinciroli, C., Stützle, T. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 77–88. Springer, Cham (2016). Scholar
  9. 9.
    Oliveira, M., Pinheiro, D., Macedo, M., Bastos-Filho, C., Menezes, R.: Better exploration-exploitation pace, better swarm: examining the social interactions. In: 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6. IEEE (2017)Google Scholar
  10. 10.
    Oliveira, M., Pinheiro, D., Macedo, M., Bastos-Filho, C., Menezes, R.: Unveiling swarm intelligence with network science-the metaphor explained. arXiv preprint arXiv:1811.03539 (2018)
  11. 11.
    Towns, J., et al.: XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16(5), 62–74 (2014). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA
  2. 2.Department of Computer ScienceGeorge Fox UniversityNewbergUSA
  3. 3.BioComplex Lab, Department of Computer ScienceUniversity of ExeterExeterUK
  4. 4.Computational Social ScienceGESIS–Leibniz Institute for the Social SciencesMannheimGermany
  5. 5.Department of Internal MedicineUniversity of CaliforniaDavisUSA
  6. 6.Polytechnic School of PernambucoUniversity of PernambucoRecifeBrazil

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