Evolutionary Optimization of Pheromone-Based Stigmergic Communication

  • Tüze Kuyucu
  • Ivan Tanev
  • Katsunori Shimohara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

Pheromone-based stigmergic communication is well suited for the coordination of swarm of robots in the exploration of unknown areas. We introduce a guided probabilistic exploration of an unknown environment by combining random movement and stigmergic guidance. Pheromone-based stigmergic communication among simple entities features various complexities that have significant effects on the overall swarm coordination, but are poorly understood. We propose a genetic algorithm for the optimization of parameters related to pheromone-based stigmergic communication. As a result, we achieve human-competitive tuning and obtain a better understanding of these parameters.

Keywords

Genetic Algorithm Multiagent System Real Robot Unknown Environment Pheromone Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tüze Kuyucu
    • 1
  • Ivan Tanev
    • 1
  • Katsunori Shimohara
    • 1
  1. 1.Information Systems DesignDoshisha UniversityKyotanabeJapan

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