Controlling an Ant Colony Optimization based Search in Distributed Datasets

B. Slivnik and U. Jovanovič (Slovenia)


distributed search, ant colony optimization.


An ant colony optimization method for searching in (pos sibly dynamic and/or unstructured) distributed datasets, as introduced by Jovanoviˇc et. al [1], is considered. This pa per 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 demon strates that one-time aggregation ants are more effective than pheromone aggregation ants.

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