A bee algorithm for multi-agent systems: Recruitment and navigation combined

In this paper we present a new, non-pheromone-based algorithm inspired by the behaviour of biological bees. The algorithm combines both recruitment and navigation strategies. We investigate whether this new algorithm outperforms pheromone-based algorithms in the task of foraging. From our experiments, we conclude that (i) the non-pheromone-based algorithm is significantly more efficient when finding and collecting food, i.e., it uses fewer iterations to complete the task; (ii) the non-pheromone-based algorithm is more scalable, i.e., it requires less computation time to complete the task, even though in small worlds, pheromone-based algorithms are faster on a time-per-iteration measure; and finally, (iii) our current non-pheromone-based algorithm is less adaptive than pheromone-based algorithms.

[1]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[2]  Paul Graham,et al.  Route learning by insects , 2003, Current Opinion in Neurobiology.

[3]  Malcolm Yoke-Hean Low,et al.  A Bee Colony Optimization Algorithm to Job Shop Scheduling , 2006, Proceedings of the 2006 Winter Simulation Conference.

[4]  K. Frisch The dance language and orientation of bees , 1967 .

[5]  R Wehner,et al.  Path integration in desert ants, Cataglyphis fortis. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Thomas S. Collett,et al.  How do insects represent familiar terrain? , 2004, Journal of Physiology-Paris.

[7]  W. Kirchner,et al.  How honeybees perceive communication dances, studied by means of a mechanical model , 1992, Behavioral Ecology and Sociobiology.

[8]  Robert D. Montgomerie Insects and Flowers: the Biology of a Partnership. Princeton University Press, Princeton, New Jersey (1985), ix, translated by M. A. Biederman-Thorson, +297. Price $35.00 , 1986 .

[9]  E. Postma,et al.  TO BEE OR NOT TO BEE : A COMPARATIVE STUDY IN SWARM INTELLIGENCE , 2006 .

[10]  Dušan Teodorović,et al.  Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .

[11]  Ida G. Sprinkhuizen-Kuyper,et al.  Robust and Scalable Coordination of Potential-Field Driven Agents , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[12]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[13]  Glenn A. Iba,et al.  A heuristic approach to the discovery of macro-operators , 2004, Machine Learning.

[14]  Fred C. Dyer Animal behaviour: When it pays to waggle , 2002, Nature.

[15]  S. Camazine,et al.  A model of collective nectar source selection by honey bees , 1991 .

[16]  R. Pfeifer,et al.  A mobile robot employing insect strategies for navigation , 2000, Robotics Auton. Syst..

[17]  Doina Precup,et al.  Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..

[18]  J. Deneubourg,et al.  The self-organizing exploratory pattern of the argentine ant , 1990, Journal of Insect Behavior.