This is the era of the internet and more than 50 percent of the population is dependent on it. Internet is collection of web pages which can be transported across it. Its size is very huge and increasing exponentially day by day. Now, around 30 percent of the overall content is duplicate. Finding relevant and required information from such a huge source of information is a very challenging task and finding top ranked results is a NP-hard problem. Majorly, the overall content can be categorized in three parts: content data, usage data and structure data. Web mining techniques and metaheuristic algorithms have been successfully applied in the literature to extract the useful information. This paper aims to provide ranking to the web links which could be utilized for restructuring of websites for business intelligence. Different features like keywords frequency, user’s navigation behaviour like unique visitors, duration stayed, access frequency, hub, and authority information has been utilized for prioritizing top-T web links. These results may be useful in different applications that include web personalization, website reorganization, recommendation systems, search engine optimization, etc. Other than this, it will also help in improving the user’s experience on websites. Based on different types of data, algorithms WLRGA, WLRBBBC, and WLRGbSA have been proposed for finding top-T web links and experimentally it has been seen that WLRGbSA is able to select top quality web links useful specially for website reorganization.