Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging

TL;DR

This paper presents a mathematical model showing how varying levels of stochasticity in ant behavior influence foraging efficiency, revealing optimal strategies depend on environmental conditions and pheromone interactions.

Contribution

It introduces a novel model linking stochasticity distribution among ants to foraging efficiency, highlighting the role of pheromone sensitivity and environmental changes.

Findings

Optimal stochasticity distribution shifts with environment

Interaction between stochasticity and pheromone path affects efficiency

Stochasticity can enhance collective foraging performance

Abstract

A mathematical model of garden ants (Laius japonicus) is introduced herein to investigate the relationship between the distribution of the degree of stochasticity in following pheromone trails and the group foraging efficiency. Numerical simulations of the model indicate that depending on the systematic change of the feeding environment, the optimal distribution of stochasticity shifts from a mixture of almost deterministic and mildly stochastic ants to a contrasted mixture of almost deterministic ants and highly stochastic ants. In addition, the interaction between the stochasticity and the pheromone path regulates the dynamics of the foraging efficiency optimization. Stochasticity could strengthen the collective efficiency when stochasticity to the sensitivity of pheromone for ants is introduced in the model.