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Biomimicry: Further Insights from Ant Colonies?

  • Francis L. W. Ratnieks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5151)

Abstract

Biomimicry means learning from nature. Well known examples include physical structures such as the Velcro fastener. But natural selection has also “engineered” mechanisms by which the components of adaptive biological systems are organized. For example, natural selection has caused the foragers in an ant colony to cooperate and communicate in order to increase the total foraging success of the colony. Ant colony optimization (ACO) is based on the pheromone trails by which many ant species communicate the locations of food in the environment around the nest. Computer algorithms based on ACO perform well in hard computational problems like the Traveling Salesman Problem. ACO algorithms normally use only a single attractive “pheromone”. However, it seems that real ants use more. The Pharaoh’s ant, Monomorium pharaonis, uses three different trail pheromones to provide short-term (volatile) and long-term attraction (non-volatile) and short-term (volatile) repellence so that foragers are directed to particular locations of the trail system where food can be collected. In addition, Pharaoh’s ants also extract information from the geometry of the trail system and have division of labour among the forager workers, some of whom specialize in laying and detecting pheromone trails. ACO takes inspiration from ant colonies but does not need to faithfully model how ant colonies solve problems. For example, in ACO “pheromone” is applied retroactively once an “ant” has returned to the nest, which is something that can easily be implemented in a computer program but is obviously something that real ants cannot do. This raises the possibility that ACO might benefit from taking further inspiration from ant colonies. Presumably, real ants use multiple information sources and communication signals for a reason.

Keywords

Ant colony optimization Pharaoh’s ant Monomorium pharaonis honey bee Apis mellifera social insects complex adaptive systems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Francis L. W. Ratnieks
    • 1
  1. 1.Laboratory of Apiculture & Social Insects Department of Biological & Environmental ScienceUniversity of SussexFalmer, BrightonUK

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