A swarm of unmanned aerial vehicles (UAVs) has many potential applications including disaster relief, search and rescue, and area surveillance. A critical factor to a UAV swarm’s success is its ability to collectively locate and pursue targets determined to be of high quality with minimal and decentralized communication. Prior work has investigated nature-based solutions to this problem, in particular the behavior of honeybees when making decisions on future nest sites. A UAV swarm may mimic this behavior for similar ends, taking advantage of widespread sensor coverage induced by a large population. To determine whether the proven success of honeybee strategies may still be found in UAV swarms in more complex and difficult conditions, a series of simulations were created in Python using a behavior modeled after the work of Cooke et al. UAV and environmental properties were varied to determine the importance of each to the success of the swarm and to find emergent behaviors caused by combinations of variables. From the simulation work done, it was found that agent population and lifespan were the two most important factors to swarm success, with preference towards small teams with long-lasting UAVs.