Intelligent Search Optimization using Artificial Fuzzy Logics

Information on the web is prodigious; searching relevant information is difficult making web users to rely on search engines for finding relevant information on the web. Search engines index and categorize web pages according to their contents using crawlers and rank them accordingly. For given user query they retrieve millions of webpages and display them to users according to web-page rank. Every search engine has their own algorithms based on certain parameters for ranking web-pages. Search Engine Optimization (SEO) is that technique by which webmasters try to improve ranking of their websites by optimizing it according to search engines ranking parameters. It is the aim of this research to identify the most popular SEO techniques used by search engines for ranking web-pages and to establish their importance for indexing and categorizing web data. The research tries to establish that using more SEO parameters in ranking algorithms helps in retrieving better search results thus increasing user satisfaction. In the accomplished research, a web based Meta search engine is proposed to aggregates search results from different search engines and rank web-pages based on new page ranking algorithm which will assign heuristic page rank to web-pages based on SEO parameters such as title tag, Meta description, sitemap etc. The research also provides insight into techniques which webmasters can use for better ranking their websites in Google and Bing. Initial results has shown that using certain SEO parameters in present ranking algorithm helps in retrieving more useful results for user queries. These results generated from Meta search engine outperformed existing search engines in terms of better retrieved search results and high precision.