Synergy in ant foraging strategies

Abstract

Collective foraging is a canonical problem in the study of social insect behavior, as well as in biologically inspired engineered systems. Pheromone recruitment is a well-studied mechanism by which ants coordinate their foraging. Another mechanism for information use is the memory of individual ants, which allows an ant to return to a site it has previously visited. There is synergy in the use of social and private information: ants with poor private information can follow pheromone trails; while ants with private information can ignore trails and instead rely on memory. We developed an agent-based model of foraging by harvester ants, and optimized the model to maximize foraging rate using genetic algorithms. We found that ants' individual memory provided greater benefit in terms of increased foraging rate than pheromone trails in a variety of food distributions. When the two strategies are used together, they out-perform either strategy alone. We compare the behavior of these models to observations of harvester ants in the field. We discuss why individual memory is more beneficial in this system than pheromone trails. We suggest that individual memory may be an important addition to ant colony optimization and swarm robotics systems, and that genetic algorithms may be useful in finding an adaptive balance between individual foraging based on memory and recruitment based on communication.