Diverse stochasticity leads a colony of ants to optimal foraging

Masashi Shiraishi; Rito Takeuchi; Hiroyuki Nakagawa; Shin I Nishimura; Akinori Awazu; Hiraku Nishimori

doi:10.1016/j.jtbi.2019.01.002

Diverse stochasticity leads a colony of ants to optimal foraging

J Theor Biol. 2019 Mar 21:465:7-16. doi: 10.1016/j.jtbi.2019.01.002. Epub 2019 Jan 7.

Authors

Masashi Shiraishi¹, Rito Takeuchi², Hiroyuki Nakagawa³, Shin I Nishimura⁴, Akinori Awazu⁵, Hiraku Nishimori⁶

Affiliations

¹ Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Kagamiyama, Higashi-hiroshima 739-8526, Japan; CREST, JST, Tokyo, Japan. Electronic address: m-shiraishi@hiroshima-u.ac.jp.
² Yahoo Japan Corporation, Tokyo, Japan.
³ Department of Mathematical Sciences, Graduate School of Engineering, Osaka Prefecture University, Sakai 599-8531, Japan.
⁴ T & S Co. Ltd., MM Park Build., 3-6-3, Minatomirai, Nishi-ku, Yokohama, Kanagawa, Japan.
⁵ Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Kagamiyama, Higashi-hiroshima 739-8526, Japan.
⁶ Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Kagamiyama, Higashi-hiroshima 739-8526, Japan. Electronic address: nishimor@hiroshima-u.ac.jp.

PMID: 30629963
DOI: 10.1016/j.jtbi.2019.01.002

Abstract

A mathematical model of garden ants (Lasius 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 the variance in the sensitivity to pheromone for ants is introduced in the model.

Keywords: Ants; Collective motion; Optimization; Stochastic foraging.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Animals
Ants / metabolism
Ants / physiology*
Feeding Behavior / physiology*
Models, Biological*
Pheromones / metabolism
Pheromones / physiology
Social Behavior*
Stochastic Processes

Substances

Pheromones