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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)

Abstract

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.

Keywords

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

Notes

Acknowledgment

The authors acknowledge support from National Science Foundation (NSF) grant No. 1560345 (http://www.nsf.gov/awardsearch/showAward?AWD_ID=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].

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Copyright information

© 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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