Adaptive ant-colony algorithm for semantic query routing

The most prevalent P2P application today is file sha ring, both among scientific users and the general public. Afundamental process in file sharing systems is the search mechanism. The unstructured nature of real-world largescale complex systems poses a challenge to the search me thods, because global routing and directory services are impractical to implement. This paper presents a new antcolony algorithm, Adaptive Neighboring-Ant Search (AdaNAS), for the semantic query routing problem (SQRP) in a P2P network. The proposed algorithm incor porates an adaptive control parameter tuning technique for runtime estimation of the time-to-live (TTL) of the ants. AdaNAS uses three strategies that take advantage of the local environment: learning, characterization, and explo ration. Two classical learning rules are used to gain ex perience on past performance using three new learning functions based on the distance traveled and the resources found by the ants. The experimental results show that the AdaNAS algorithm outperforms the NAS algorithm where the TTLvalue is not tuned at runtime.