By Topic

Query Optimization in Relevance Feedback Using Hybrid GA-PSO for Effective Web Information Retrieval

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ibrahim, S.N.A. ; Intell. Software Syst. Res. Lab. (ISSLab), Univ. Teknol. Malaysia, Skudai ; Selamat, A. ; Selamat, M.H.

Due to the rapid growth of Web pages available on the Internet recently, searching a relevant and up-to-date information has become a crucial issue. Conventional search engines use heuristics to determine which Web pages are the best match for a given keyword. Results are obtained from a database that is located at their local server to provide fast searching. However, to search for the relevant and related information needed is still difficult and tedious. By using the genetic algorithm (GA) in relevance feedback, this paper presents a model of hybrid GA-particle swarm optimization (HGAPSO) based query optimization for Web information retrieval. We expanded the keywords to produce the new keywords that are related to the user search. Experimental results demonstrate that it is very effective to improve the search of the relevant web pages using the HGAPSO.

Published in:

Modelling & Simulation, 2009. AMS '09. Third Asia International Conference on

Date of Conference:

25-29 May 2009