Jini Specification
Ant Colony Optimization
Agent-Based Load Balancing on Homogeneous Minigrids: Macroscopic Modeling and Characterization
IEEE Transactions on Parallel and Distributed Systems
Ant-Based Clustering and Topographic Mapping
Artificial Life
The complexity of static data replication in data grids
Parallel Computing
Toward nature-inspired computing
Communications of the ACM
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An ant colony optimization method for searching in (possibly dynamic and/or unstructured) distributed datasets, as introduced by Jovanovič 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.