“Inspiration for Optimization from Social Insect Behaviour”, Nature, 2000.

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Ethologists use modelling to understand animal behaviour. Recent research in social insect behaviour suggests that models based on self-organization can help explain how complex colony-level behaviour emerges out of interactions among individual insects. Although the goal of a modeller is generally to understand the living, a model can in principle be explored beyond the biologically plausible. Although biology does not necessarily benefit from such an exploration, computer scientists and engineers have been able to transform models of social insect collective behaviour into useful optimization and control algorithms. This new line of research concerns the transformation of knowledge about how social insects collectively solve problems into artificial problem-solving techniques-producing a form of Artificial Intelligence, or Swarm Intelligence, in which the underlying model of intelligence is the collective intelligence of a social insect colony. Some of the techniques of Swarm Intelligence are now maturing. Optimization and control algorithms inspired by models of co-operative food retrieval in ants have been unexpectedly successful and have become known in recent years as Ant Colony Optimization (ACO) and Ant Colony Routing (ACR). Real-world implementations are emerging. Other techniques, inspired by co-operative brood sorting by ants or task allocation in social wasps, are still in a preliminary, proof-of-concept stage, with no systematic benchmarking of their performance. ACO and ACR will be described in more detail here.


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