Ant colonies range in size from a few dozen to tens of millions of ants. The existence of such a range of colony sizes suggests that ants have evolved scalable foraging strategies. We measure foraging efficiency of desert seed-harvesting ants, in simulations designed to emulate ant foraging behaviors, and in robotic swarms with foraging algorithms that mimic ant behaviors. We find a balance between the use of remembered private information and communicated public information to maximally exploit resources. Evolutionary algorithms demonstrate that the best foraging strategy depends both on resource distribution and colony size. This work suggests that both natural and artificial evolutionary processes can fine tune a simple set of individual behaviors into a scalable, robots and flexible foraging strategy for both ants and robots.
scaling theory, ant forgaging behaviors, swarm robotics
Letendre and Moses. (2013)
Synergy in ant foraging strategies: memory and communication alone and in combination.
Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference.
Hecker and Moses. (2013)
An evolutionary approach for robust adaptation of robot behavior to sensor error.
GECCO '13 Companion Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion.