PSO based Web Documents Prioritization for Adaptive Websites using multi-Criteria

When a user searches for a query using ad-hoc keywords in any search engine, billions of pages are returned in response to user’s query on internet while user needs a few applicable and required pages. Then there we need to prioritize some top ranked web pages. Finding some top ranked web pages out of huge number of returned results a very challenging task and is NP-Hard problem. For an ordinary user query, millions of results are returned. Many of these pages are irrelevant. So, there is a need to rank these web pages using multi-criteria like keyword frequency, hub and authority values, active time spent on web pages, unique visitors. optimization algorithms give an optimal and acceptable results which could be used for search engine optimization, web recommendation system, web personalization, website reorganization and many more purposes. There are several metaheuristic algorithms used in the literature to address such problems like Particle Swarm optimization (PSO), term frequency inverse document frequency model(TF-IDF) ACO, HBO, FO, TLBO etc. In this paper PSO based optimization using multi criteria has been proposed for prioritizing web pages. Experimentally it has been shown that PSO based prioritization gives better ranked pages in comparison to popular GA based approach.