Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Simulating swarm intelligence in honey bees: foraging in differently fluctuating environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An Individual-Based Model of Task Selection in Honeybees
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Optimisation of a honeybee-colony's energetics via social learning based on queuing delays
Connection Science - Social Learning in Embodied Agents
Get in touch: cooperative decision making based on robot-to-robot collisions
Autonomous Agents and Multi-Agent Systems
The “dance or work” problem: why do not all honeybees dance with maximum intensity
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
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
Computers and Electronics in Agriculture
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Honey bees show the impressive ability to choose collectively (through swarm intelligence) between nectar sources of different quality by selecting the energetically optimal one. We here present results from a multi-agent simulation of a cohort of foraging bees. The model, which is built on proximate individual mechanisms, leads to interesting results on the (global) colony level. The simulation allows us to investigate collective foraging decisions in a variety of experimental setups that can be reproduced experimentally with real bees. Because our model allows us to project the daily net honey gain of the simulated honey bee colony, it enables us to explore the economic results of foraging decisions, even in a fluctuating environment. We used the model to investigate the dynamics and efficiency of a bee colony's decentralized decision system in terms of minimizing the potential cost of lost nectar income due to changes in food quality in a fluctuating environment.