GA Directed Self-Organized Search and Attack UAV Swarms
Ian Price, Gary Lamont · 2006
Self-organization offers many potential benefits to autonomous multi-UAV systems. This research investigates the use of a self-organization (SO) framework for evolving UAV swarm behavior. This SO framework is used to design a UAV swarm simulation with evolving behavior. The swarm behavior is then evolved using a genetic algorithm (GA) to successfully locate and destroy retaliating stationary targets. This system is tested using both a set of strictly homogeneous UAVs and heterogeneous UAVs with intriguing results