Web prefetching is based on web caching and attempts to reduce user-perceived latency. Unlike on-demand caching, web prefetching fetches objects and stores them in advance, hoping that the prefetched objects are likely to be accessed in the near future and such accesses would be satisfied from the cache rather than by retrieving the objects from the web server. This paper reviews the popular prefetching algorithms based on Popularity, Good Fetch, APL characteristic, and Lifetime, and then makes the following contributions. (1) The paper proposes a family of prefetching algorithms, Objective-Greedy prefetching, wherein each algorithm greedily prefetches those web objects that give the highest performance as per the metric that it aims to improve. (2) The paper shows the results of a performance analysis via simulations, comparing the objective-greedy algorithms with the existing algorithms in terms of the respective objectives - the hit rate, bandwidth, and the H/B metrics. The proposed prefetching algorithms are seen to provide the best objective-based performance. (3) The paper also proves that the algorithms based on Good Fetch and on the APL characteristic, although using different criteria, are equivalent in terms of their choice of objects selected for prefetching.