Mining Top – k Ranked Webpages Using Simulated Annealing and Genetic Algorithms

Abstract

Searching on the Internet has grown in importance over the last few years, as huge amount of information is invariably accumulated on the Web. The problem involves locating the desired information and corresponding URLs on the WWW. With billions of webpages in existence today, it is important to develop efficient means of locating the relevant webpages on a given topic. A single topic may have thousands of relevant pages of varying popularity. Top – k document retrieval systems identifies the top – k ranked webpages pertaining to a given topic. In this paper, we propose an efficient top-k document retrieval method (TkRSAGA), that works on the existing search engines using the combination of Simulated Annealing and Genetic Algorithms. The Simulated Annealing is used as an optimized search technique in locating the top-k relevant webpages, while Genetic Algorithms helps in faster convergence via parallelism. Simulations were conducted on real datasets and the results indicate that TkRSAGA outperforms the existing algorithms.