Multiple ant colonies algorithm based on colony level interactions

Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, kno wn as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies. chemical subs tances, called pheromones, are used as a way to communicate important infor mation on global behavior. For example. ants looking for food lay the way b ack to their nest with a specific type of pheromone. Other ants can follow the pheromone trail and find their way to baits efficiently. In 1991, Color ni et al. proposed the ant algorithm for Traveling Salesman Problems (TSPs) by using the analogy of such foraging behavior and pheromone communication . In the ant algorithm, there is a colony consisting of many simple ant age nts that continuously visit TSP cities with opinions to prefer subtours con necting near cities and they lay strong pheromones. The ants completing the ir tours lay pheromones of various intensities with passed subtours accordi ng to distances. Namely, subtours in TSP tourns that have the possibility o f being better tend to have strong pheromones, so the ant agents specify go od regions in the search space by using this positive feedback mechanism. I n this paper, we propose a multiple ant colonies algorithm that has been ex tended from the ant algorithm. This algorithm as several ant colonies for s olving a TSP, while the original has only a single ant colony. Moreover, tw o kinds of pheromone effects, positive and negative pheromone effects, are introduced as the colony-level interactions. As a result of colony-level in teractions, the colonies can exchange good schemata for solving a problem a nd can maintain their own variation in the search process. The proposed alg orithm shows better performance than the original algorithm with almost the same agent strategy used in both algorithms except for the introduction of colony-level interactions.