Honeybee Optimisation – An Overview and a New Bee Inspired Optimisation Scheme

  • Konrad Diwold
  • Madeleine Beekman
  • Martin Middendorf
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 8)


In this chapter we discuss honey bee optimisation algorithms, which constitute a new trend in the field of swarm intelligence. As the name suggests this class of algorithms is based on the behaviour of honeybees. Current algorithms are based on either of two principles: foraging or mating. Algorithms based on mating utilize the behavioral principles of polyandry found in honey bees and algorithms based on foraging apply the principles of collective resource exploration/exploitation of bee colonies in the context of optimisation.

After reviewing the biological foundations, the existing bee optimisation algorithms will be outlined. We also discuss the potential of bee nest-site selection as a source for new bee-inspired optimization algorithms. A detailed model based on the honeybee nest-site selection process found in nature is described and empirically tested regarding its optimisation behaviour. Building on this model a new algorithmic scheme for bee-inspired optimization algorithms - Bee Nest-Site Selection Scheme (BNSSS) - is proposed.


bee algorithms nature-inspired algorithms combinatorial optimisation function optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Konrad Diwold
    • 1
  • Madeleine Beekman
    • 2
  • Martin Middendorf
    • 1
  1. 1.Department of Computer ScienceUniversität LeipzigGermany
  2. 2.Behaviour and Genetics of Social Insects Lab and, Center for Mathematical Biology School of Biological SciencesThe University of SydneySydneyAustralia

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