Bee System with inhibition Pheromones

In previous work, we developed a foraging algorithm inspired by the behavior of biological bees. In relatively unobstructed environments, this algorithm has been shown to outperform an ant-inspired algorithm in terms of efficiency and scalability. However, due to its low adaptability outside of the hive, our bee-inspired algorithm displays weaker performance in more obstructed environments due to its nature to always use a straight, direct path to its destination without taking obstacles into account. In this paper, we present Bee System with inhibition Pheromones, a new hybrid algorithm based on the recruitment and navigation strategies of bees and extended with inhibition pheromones to enhance adaptability. Moreover, wall following has been implemented to improve obstacle avoidance. We show that the hybrid algorithm truly combines thebest of both worlds' and manages to outperform both our ant- inspired algorithm as our bee-inspired algorithm in a variety of experimental settings.

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

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

[3]  Cees Witteveen,et al.  Multi-agent Planning An introduction to planning and coordination , 2005 .

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

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

[6]  Panta Lucic,et al.  Computing with Bees: Attacking Complex Transportation Engineering Problems , 2003, Int. J. Artif. Intell. Tools.

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

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

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

[10]  N. Cowan,et al.  Task-level control of rapid wall following in the American cockroach , 2006, Journal of Experimental Biology.

[11]  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 .

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

[13]  Karl Tuyls,et al.  A bee algorithm for multi-agent systems: Recruitment and navigation combined , 2007 .

[14]  Ahmed F. Zobaa,et al.  Neural Network Applications in Electrical Engineering , 2007, Neurocomputing.

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

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

[17]  Karl Tuyls,et al.  Bee behaviour in multi-agent systems: a bee foraging algorithm , 2005 .

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

[19]  H. Stanley,et al.  Lévy flights in random searches , 2000 .

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

[21]  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.

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