• Aug 09, 2018 News![CFP] The annual meeting of IJFCC Editorial Board, ICCTD 2019, will be held in Prague, Czech Republic during March 2-4, 2019.   [Click]
  • Aug 09, 2018 News!IJFCC Vol. 6, No. 1-No. 3 has been indexed by EI (Inspec).   [Click]
  • Dec 24, 2018 News!The papers published in Vol.7, No.1-No.2 have all received dois from Crossref.
General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2013 Vol.2(6): 595-599 ISSN: 2010-3751
DOI: 10.7763/IJFCC.2013.V2.234

Web Information Retrieval Using Genetic Algorithm-Particle Swarm Optimization

Priya I. Borkar and Leena H. Patil
Abstract—The rapid growth of web pages available on the Internet recently, searching relevant and up-to-date information has become a crucial issue. Information retrieval is one of the most crucial components in search engines and their optimization would have a great effect on improving the searching efficiency due to dynamic nature of web it becomes harder to find relevant and recent information. That’s why more and more people begin to use focused crawler to get information in their special fields today. Conventional search engines use heuristics to determine which web pages are the best match for a given keyword. Earlier 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. This paper presents a model of hybrid Genetic Algorithm -Particle Swarm Optimization (HGAPSO) for Web Information Retrieval. Here HGAPSO expands the keywords to produce the new keywords that are related to the user search.

Index Terms—Genetic algorithm, information retrieval system, particle swarm optimization.

The authors are with the Priyadarshini Institute of Engineering and Technology, Nagpur, India (e-mail:priyas1586@yahoo.co.in, harshleena23@rediffmail.com).


Cite:Priya I. Borkar and Leena H. Patil, "Web Information Retrieval Using Genetic Algorithm-Particle Swarm Optimization," International Journal of Future Computer and Communication vol. 2, no. 6, pp. 595-599, 2013.

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