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A mathematical model of garden ants (Laius japonicus) is introduced herein toinvestigate the relationship between the distribution of the degree ofstochasticity in following pheromone trails and the group foraging efficiency.Numerical simulations of the model indicate that depending on the systematicchange of the feeding environment, the optimal distribution of stochasticityshifts from a mixture of almost deterministic and mildly stochastic ants to acontrasted mixture of almost deterministic ants and highly stochastic ants. Inaddition, the interaction between the stochasticity and the pheromone pathregulates the dynamics of the foraging efficiency optimization. Stochasticitycould strengthen the collective efficiency when stochasticity to thesensitivity of pheromone for ants is introduced in the model.

Diverse Stochasticity Leads a Colony of Ants to Optimal Foraging