Bee-inspired foraging in an embodied swarm

Authors:
Sjriek Alers;Daan Bloembergen;Daniel Hennes;Steven de Jong;Michael Kaisers;Nyree Lemmens;Karl Tuyls;Gerhard Weiss
Affiliations:
Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands;Maastricht University, Maastricht, The Netherlands
Venue:
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Year:
2011

Citing 4
Cited 0

Stigmergic landmark foraging

Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Stigmergic landmark routing: a routing algorithm for wireless mobile ad-hoc networks

Proceedings of the 12th annual conference on Genetic and evolutionary computation
Bee behaviour in multi-agent systems: a bee foraging algorithm

ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Ant colony optimization

IEEE Computational Intelligence Magazine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation of environmental parameters (e.g. pheromones).