Abstract
In this paper we present a new, non-pheromone-based algorithm inspired by the behaviour of bees. The algorithm combines both recruitment and navigation strategies. We investigate whether this new algorithm outperforms pheromone-based algorithms, inspired by the behaviour of ants, in the task of foraging. From our experiments, we conclude that (i) the bee-inspired algorithm is significantly more efficient when finding and collecting food, i.e., it uses fewer iterations to complete the task; (ii) the bee-inspired algorithm is more scalable, i.e., it requires less computation time to complete the task, even though in small worlds, the ant-inspired algorithm is faster on a time-per-iteration measure; and finally, (iii) our current bee-inspired algorithm is less adaptive than ant-inspired algorithms.
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References
von Frisch, K.: The dance language and orientation of bees. Harvard University Press, Cambridge, Massachusetts (1967)
Lambrinos, D., Möller, R., Labhart, T., Pfeifer, R., Wehner, R.: A mobile robot employing insect strategies for navigation. Robotics and Autonomous Systems 30(1-2), 39–64 (2000)
Müller, M., Wehner, R.: Path integration in desert ants, Cataglyphis Fortis. Proceedings of the National Academy of Sciences 85(14), 5287–5290 (1988)
Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: algorithms, applications, and advances. Technical report, Université of Libre de Bruxelles (2000)
Lucic, P., Tedorovic, D.: Computing with bees: attacking complex transportation engineering problems. International Journal on Artificial Intelligence Tools 12, 375–394 (2003)
Nakrani, S., Tovey, C.: On honey bees and dynamic server allocation in internet hosting centers. Adaptive Behaviour 12, 223–240 (2004)
Chong, C., Low, M.H., Sivakumar, A., Gay, K.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 2006 Winter Simulation Conference, Monterey, CA USA, pp. 1954–1961 (2006)
Teodorovic, D., Dell’ Orco, M.: Bee colony optimization: A cooperative learning approach to complex transportation problems. In: Proceedings of the 16th Mini - EURO Conference and 10th Meeting of EWGT (2006)
Lemmens, N.: To bee or not to bee: A comparative study in swarm intelligence. Master’s thesis, Maastricht University, The Netherlands (2006)
Michelsen, A., Andersen, B., Storm, J., Kirchner, W., Lindauer, M.: How honeybees perceive communication dances, studied by means of a mechanical model. Behavioral Ecology and Sociobiology 30(3-4), 143–150 (1992)
Dyer, F.: When it pays to waggle. Nature 419, 885–886 (2002)
Camazine, S., Sneyd, J.: A model of collective nectar source by honey bees: selforganization through simple rules. Journal of Theoretical Biology 149, 547–571 (1991)
Barth, F.: Insects and flowers: The biology of a partnership. Princeton University Press, Princeton, New Jersey (1982)
Collett, T.S., Graham, P., Durier, V.: Route learning by insects. Current Opinion in Neurobiology 13(6), 718–725 (2003)
Collett, T., Collett, M.: How do insects represent familiar terrain. Journal of Physiology 98, 259–264 (2004)
Viswanathan, G.M., Afanasyevc, V., Buldyrev, S.V., Havlin, S., da Luze, M.G.E., Raposof, E.P., Stanley, H.E.: Lévy flights in random searches. Physica A: Statistical Mechanics and its Applications 282, 1–12 (2000)
Sutton, R.S., Precup, S., Singh, S.: Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112, 181–211 (1999)
Iba, G.A.: A heuristic approach to the discovery of macro-operators. Machine Learning 3, 285–317 (1989)
Deneubourg, J., Aron, S., Goss, S., Pasteels, J.: The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behaviour 3, 159–168 (1990)
de Jong, S., Tuyls, K., Sprinkhuizen-Kuyper, I.: Robust and scalable coordination of potential-field driven agents. In: Proceedings of IAWTIC/CIMCA, Sydney (2006)
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Lemmens, N., de Jong, S., Tuyls, K., Nowé, A. (2008). Bee Behaviour in Multi-agent Systems. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds) Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning. AAMAS ALAMAS ALAMAS 2005 2007 2006. Lecture Notes in Computer Science(), vol 4865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77949-0_11
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DOI: https://doi.org/10.1007/978-3-540-77949-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77947-6
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