Controlling an ant colony optimization based search in distributed datasets

An ant colony optimization method for searching in (possibly dynamic and/or unstructured) distributed datasets, as introduced by Jovanovic et. al [1], is considered. This paper provides two new results. Firstly, it describes how this method can easily be controlled by using different kinds of ants for aggregation of data found: "classic" pheromone aggregation ants should be used if network load caused by a distributed search should be strictly kept within given limits, while one-time aggregation ants should be used if the search process should react quickly due to changes in a dynamic distributed dataset. Secondly, it demonstrates that one-time aggregation ants are more effective than pheromone aggregation ants.