Emergence of population structure in socio-cognitively inspired ant colony optimization


  • Aleksander Byrski AGH University of Science and Technology
  • Ewelina Swiderska AGH University of Science and Technology
  • Jakub Lasisz AGH University of Science and Technology
  • Marek Kisiel-Dorohinicki AGH University of Science and Technology
  • Tom Lenaerts Universite Libre de Bruxelles
  • Dana Samson Catholique Universite de Louvain
  • Bipin Indurkhya Jagiellonian University




A metaheuristic proposed by us recently, Ant Colony  Optimization (ACO) hybridized with socio-cognitive inspirations, turned out to generate interesting results compared to classic ACO. Even though it does not always find better solutions to the considered problems, it usually finds sub-optimal solutions  usually. Moreover, instead of a trial-and-error approach to configure the parameters of the ant species in the population, in our approach, the actual structure of the population emerges from predefined species-to-species ant migration strategies. Experimental results of our approach are compared against classic ACO and selected socio-cognitive versions of this algorithm.


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How to Cite

Byrski, A., Swiderska, E., Lasisz, J., Kisiel-Dorohinicki, M., Lenaerts, T., Samson, D., & Indurkhya, B. (2018). Emergence of population structure in socio-cognitively inspired ant colony optimization. Computer Science, 19(1), 81. https://doi.org/10.7494/csci.2018.19.1.2594




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