Volatility and spatial distribution of resources determine ant foraging strategies

Abstract

Social insect colonies have evolved collective foraging strategies that consist of many autonomous individuals operating without centralized control. The ant colony optimization (ACO) family of algorithms mimics this behavior to approximate solutions to computationally difficult problems. ACO algorithms focus on pheromone recruitment, which is only one of several known biological foraging strategies. Here, we use a spatial agent-based model to simulate three foraging strategies: pheromone recruitment, nest recruitment, and random search. We compare their performance across two environmental dimensions: spatial distribution of food resources and resource volatility. We find that pheromone recruitment performs only marginally better than the simpler nest recruitment strategy in most environments. Further, both strategies become progressively less efficient as resource dispersion and volatility increase. In the extreme, with highly dispersed or volatile resources, the simplest strategy of all, random search, outperforms the other two. Our results suggest that in many environments, pheromone-based strategies may not be required and that simpler methods like random search or nest recruitment may be sufficient, both for biological ants and computational methods.