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. © 2012 Springer-Verlag.
CITATION STYLE
Kuyucu, T., Tanev, I., & Shimohara, K. (2012). Evolutionary optimization of pheromone-based stigmergic communication. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7248 LNCS, pp. 63–72). https://doi.org/10.1007/978-3-642-29178-4_7
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