The Embedding Algorithm of an Individual Ant Behaviour (Agent) into an Ant Colony Systems

Abstract

Ants are fascinating creatures that demonstrate the ability to find food and bring it back to their nest. Their ability as a colony, to find paths to food sources has inspired the development of algorithms known as Ant Colony Systems (ACS). The principle of cooperation forms the backbone of such algorithms, commonly used to find solutions to problems such as the Traveling Salesman Problem (TSP). Ants communicate to each other through chemical substances called pheromones. Modeling individual ants’ ability to manipulate this substance can help an ACS find the best solution. This paper introduces a Dynamic Ant Colony System with three-level updates (DACS3) which embeds individual ant behavior(agent) in existing ACS. Experiments were conducted to observe single ant behavior in a colony of Malaysian House Red Ants. Such behaviour was incorporated into the DACS3 algorithm. We benchmarked the performance of DACS3 against several Ant Colony Optimizations (ACO) algorithms on TSP instances ranging from 14 to 100 cities. The result showed that embedding a simple behaviour into an ant improved an ACS algorithm.