Research on Optimization Algorithm for Deep Web Query
Deep Web query is a request sent to data sources after analyzing the properties and plentiful semantic information of interfaces to get the particular data,qualities of which can affect the relativity of results and the query cost.To optimize the query,this paper proposes an optimizing algorithm based on quantum genetic algorithm,which takes the binary string of Deep Web query as input that is encoded in quantum state.The algorithm introduces multi-swarms parallel searching mechanism in sphere solution spaces with a permutation operator among chromosomes and a quantum mutation operator to enrich the population and promote the efficiency of searching optimal solution.The experiment results indicate that comparing with other genetic algorithms,the algorithm performs well in R-Precision and coverage,and can reduce query count effectively.