Swarm intelligence
Ant Colony Optimization
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Optimisation of a honeybee-colony's energetics via social learning based on queuing delays
Connection Science - Social Learning in Embodied Agents
Economic optimisation in honeybees: adaptive behaviour of a superorganism
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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A honeybee colony has to choose among several nectar sources in the environment, each fluctuating in quality over time. Successful forager bees return to the hive and perform dances to describe the food sources they have found. Each dancer tries to recruit other forager bees to fly to the source it has found. Some individual dancers clearly dance longer for higher quality sources, other dancers distinguish little between poor and good sources; presumably the differences are genetically based [6]. Our multi-agent simulation showed that this individual heterogeneity results in optimal collective exploitation of the environment. Under all tested environmental conditions near-natural heterogeneous colonies worked more efficiently than artificially homogeneous ones. In heterogeneous colonies, dances last sufficiently long to recruit an appropriate number of waiting dancefollowing bees. In homogeneous colonies with good discriminating bees, the dances last longer than is efficient; the extra dancing decreases net food gains per time.