Local Survival Rule for Steer an Adaptive Ant-Colony Algorithm in Complex Systems
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Abstract
The most prevalent P2P application today is file sharing, both among scientific users and the general public. A fundamental process in file sharing systems is the search mechanism. The unstructured nature of real-world large-scale complex systems poses a challenge to the search methods, becasuse global routing and directory services are impractical to implement. In this paper, a new ant-colony algorithm, Adaptive Neighboring-Ant Search (AdaNAS), for the semantic query routing problem (SQRP) in a P2P network is presented. The proposed algorithm incorporates 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 exploration. Two classical learning rules are used to gain experience 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 TTL value is not tuned at runtime.
Keywords
parameter tuning search algorithm peer-to-peer adaptive algorithm local environment ant-colony algorithmsPreview
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