Research On Distributed Database Query Optimization Based On Multi - Ant Colony Genetic Algorithm

In nowadays’ information explosion society, information springs up massively,which leads to increase of application demands for distributed database. As an important procedure of distributed database, it is very necessary to increase its efficiency of inquiry. This paper mainly studies how to find a reasonable and effective calculation method to optimalize the inquiry efficiency.Starting from the basic concept and theory of the relevant technology for distributed database, this paper reflects the insufficiency for the current traditional calculation methods, mainly analyzes the insufficiency of Genetic Algorithm and Ant Colony Algorithms in inquiry optimalization, and brings in the multiple Ant Colony Algorithms concept under the basis of ordinary ant colony after further thinking,proposes the interactive learning mechanism among multiple ant colonies, decreases the probability of partly optimalization of the algorithm, increase the competence of optimized algorithm; and then puts forwards the combined optimalization algorithm between multiple ant colony algorithm and genetic algorithm, which applies the genetic algorithm to break the blindness of the previous search of multiple ant colony algorithm and combines the excellent optimalized competence of multiple ant colony algorithm so as to finally reach the purpose of increasing inquiry efficiency. The final experiment results indicate that during tackling with the inquiry question of distributed database, improved optimalized algorithm has obvious improvement in inquiry costs compared with basic ant colony algorithm.