Advertisement

The Spatial Pheromone Signal for Ant Colony Optimisation

  • Ilija Tanackov
  • Dragan Simić
  • Jelena Mihaljev-Martinov
  • Gordan Stojić
  • Siniša Sremac
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

The effect of the passive insecticide on the ant colony Monomorius pharaonis is localised with minor losses – only one ant. The information on the insecticide location is transferred through the colony in all directions with great speed. After deserting the basic trail, a rapid consolidation of the new ant colony is probably established by the spatial pheromone signal. A simulation model for the time calculation and the number of ants necessary for the formation of the shortest way between the nest and the fictive food source was formed. The basic ant performances have a prevailing part in the shortest trail formation and those are: the range of the radius pheromone signal and the intensity of the pheromone trail evaporation.

Keywords

Monomiorium pharaonis Ant colony optimisation simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Darwin, C.: Origin of certain insticts. Nature 7, 417–418 (1873)CrossRefGoogle Scholar
  2. 2.
    Markle, T., Rost, M., Alt, W.: Egocentric path integration and their application to desert arthropods. Journal of Theoretical Biology 240(3), 385–399 (2006)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Stepankova, K., Pastalkova, E., Kalova, E., Kalina, M., Bures, J.: A battery of test quantitative examination of idiothetic and allothetic place navigation modes in humans. Behavioural Brain Research 147(1-2), 95–105 (2003)CrossRefGoogle Scholar
  4. 4.
    Ma (Sam), Z., Krings, A.W.: Insect sensory system inspired computing and communications. Ad Hoc Networks 7(4), 742–755 (2009)CrossRefGoogle Scholar
  5. 5.
    Jackson, D.E., Chaline, N.: Moduluation of pheromone trail strength with food quality in Pharaon’s ant, Monomorius pharaonis. Animal behaviour 74(3), 463–470 (2007)CrossRefGoogle Scholar
  6. 6.
    Sumpter, D.J.T., Beekman, M.: From nonlinearity to optimality: Pheromone trail foraging by ants. Animal Behaviour 66(2), 273–280 (2003)CrossRefGoogle Scholar
  7. 7.
    Niven, J.E.: Invertebrate Memory: Wide-Eyed Ants Retrieve Visual Snapshots. Current Biology 17(3), R85–R87 (2007)Google Scholar
  8. 8.
    Planque, R., Dechaume-Moncharmont, F.X., Franks, N.R., Kovacs, T., Marshall, J.A.R.: Why do house-hunting ants recruit in doth directions. Naturwissenschaften 94(11), 911–918 (2007)CrossRefGoogle Scholar
  9. 9.
    Franks, N.R., Britton, N.F., Sendova-Franks, A.B., Denny, A.J., Soans, E.L., Brown, A.P., Cole, R.E., Havardi, R.J., Griffiths, C.J., Ellis, S.R.: Centrifugal waste disposal and the optimization of ant nest craters. Animal Behaviour 67(5), 965–973 (2004)CrossRefGoogle Scholar
  10. 10.
    Teodorović, D.: Swarm intelligence systems for transportation engineering: Principles and applications. Transportation Research Part C: Emerging Technologies 16(6), 651–667 (2008)CrossRefGoogle Scholar
  11. 11.
    Wu, Z., Zhao, N., Ren, G., Quan, T.: Population declining ant colony optimization algorithm and its applications. Expert Systems with Applications 36(3), 6276–6281 (2009)CrossRefGoogle Scholar
  12. 12.
    Gutjahr, W.J., Sebastiani, G.: Runtime Analysis of Ant Colony Optimisation with Best-So-Far Reinforcement. Methodology and Computing in Applied Probability 10(3), 409–433 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Pop, C.P., Pintea, C.M., Sitar, C.P.: An Ant-Based Heuristic for the Railway Traveling Salesman Problem. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 702–711. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Smith III, J.F., Nguyen, T.V.H.: Autonomous and cooperative robotic behavior based on fuzzy logic and genetic programming. Integrated Computer-Aided Engineering 14(2), 141–159 (2007)Google Scholar
  15. 15.
    Jackson, D., Holcombe, M., Ratnieks, F.: Coupled computational simulation and empirical research into the foraging system of Pharaoh’s ant (Monomorium pharaonis). Biosystems 76(1-3), 101–112 (2004)CrossRefGoogle Scholar
  16. 16.
    Vittori, K., Talbot, G., Gautrais, J., Fourcassié, V., Araújo, A.F.R., Theraulaz, G.: Path efficiency of ant foraging trails in an artificial network. Journal of Theoretical Biology 239(4), 507–515 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ilija Tanackov
    • 1
  • Dragan Simić
    • 1
  • Jelena Mihaljev-Martinov
    • 2
  • Gordan Stojić
    • 1
  • Siniša Sremac
    • 1
  1. 1.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia
  2. 2.Faculty of MedicineUniversity of Novi SadNovi SadSerbia

Personalised recommendations