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Due to the rapid growth of Web pages available on the Internet recently, searching a relevant and up-to-date information has become a crucial issue. Conventional search engines use heuristics to determine which Web pages are the best match for a given keyword. 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. By using the genetic algorithm (GA) in relevance feedback, this paper presents a model of hybrid GA-particle swarm optimization (HGAPSO) based query optimization for Web information retrieval. We expanded the keywords to produce the new keywords that are related to the user search. Experimental results demonstrate that it is very effective to improve the search of the relevant web pages using the HGAPSO.