LAAPS: an efficient file-based search in unstructured peer-to-peer networks using reinforcement algorithm

An efficient searching algorithm in the absence of control over the object locations is a challenging issue in the design of unstructured peer-to-peer (P2P) networks. In this paper, we report files searching constraints in the unstructured P2P systems. Hence, we exploit learning automata adaptive probabilistic search (LAAPS) algorithm for those constraints which are distributed entirely with adequate bandwidth. LAAPS optimize the learning automata using file container nodes which are leveraged with the types of file. In the simulation, we compare the proposed algorithm with the random probabilistic walk, adaptive search algorithm, and improved adaptive probabilistic search algorithm. The numerical result demonstrates that LAAPS improves the number of hits per query, achieving high success rate with significant message reduction.