Author/Authors :
Ghorbani، Mahdi نويسنده Department of Computer & IT Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran , , Saghiri، Ali Mohammad نويسنده Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran , , Meybodi، Mohammad Reza نويسنده Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran ,
Abstract :
In order to file sharing as a popular application of unstructured peer to peer networks, finding a certain amount of data in each node, needs performing an appropriate search method. In this paper, we propose a new version of k-random walk algorithm using learning automata. In the proposed method, the value of k for k-random walk is not selected randomly but it is selected in an adaptive manner. It is decided which k walkers are more useful to be selected in order to keep on the search according to past experience of each node. Simulation results show that the novel search algorithm improves the number of hits per query, success rate and the delay of objects discovery in comparison with the k-random walk algorithm.
