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Study of ants' traffic organisation under crowded conditions using individual-based modelling and evolutionary computation

Repulsive interactions of black garden ants (Lasius Niger) has been found to be critical for preventing congestion and maintaining optimal food return rate in ant colony. Previously, mathematical models have been built to study the effect of the repulsive interactions on the path selection decision of ants. However, the detailed mechanisms behind the interactions are still poorly understood. For the first time, we developed an evolvable individual-based model to simulate foraging ants with the repulsive interactions, to investigate the underlying mechanisms and its effects on the overall food return rate of the ant colony. We employed a two-phase evolutionary process using a Genetic Algorithm: we firstly evolved a model with trail following behaviour in an open environment in order to make this behaviour more biologically realistic. Then based on the evolved model, the repulsive interactions were introduced and evolved on a double-bridge environment in order to get an optimal effect on the food return rate in crowded situation. Our model is sufficient enough to reveal the details of the possible underlying mechanisms of the repulsive interactions and its effect on the transportation efficiency.