Distributed Database Query Optimization Based On Improved Genetic Algorithm

As the information technology developing rapidly in recent years,a lot of data,like text,pictures,audio,video etc are produced every day,big data's research also lead to the majority of scholars attention.The study of distributed database has become a hot topic in the era of big data.While the query is one of the most frequent operations in a distributed database,it is very necessary to improve its efficiency.With the fact that the amount of data grows constantly,the requirement of the distributed database query is getting higher and higher,it is imperative to design an efficient distributed query solution.This paper first introduces the basic concept of distributed database and the commonly used query optimization technology,the characteristics of the genetic algorithm and the implementation process,aiming at the shortcomings of FCM algorithm,an optimization scheme is proposed,the improved genetic algorithm is realized by using the optimized FCM algorithm and genetic algorithm,in the query of distributed database,the improved genetic algorithm is used to optimize in this paper.The main research work of this paper can be summarized as follows:(1)FCM algorithm is a fuzzy clustering algorithm based on objective function,which is mainly used for clustering analysis of data,with mature theory and being widely used,it is an excellent clustering algorithm.But the effect of FCM algorithm is often affected by the initial clustering center,and the convergence results tend to fall into the local optimal problem,this paper presents an optimized FCM algorithm solution.The optimized FCM clustering algorithm adopts the rule that selects the initial clustering center to obtain the clustering result as a global optimal solution.Simulation experiments shows that compared with the traditional FCM algorithm,the optimized FCM algorithm has higher accuracy and less number of iterations.(2)In view of the shortcomings of the traditional genetic query algorithm,this paper adopts the method of setting multiple probabilities.All the individuals of the present age are divided into three categories by the optimized FCM clustering algorithm,each category is set to a different probability:Setting a higher probability of crossover and mutation for individuals with lower levels,and increasing the ability to generate new individual structures;Setting a lower probability of crossover and mutation for individuals with higher levels,and reducing the possibility that the preference gene is destroyed;Setting the probability of crossover and mutation between the higher and lower selection probabilities for individual settings in the middle of the level.In this way,it better resolved the problem that the selection and variation probability setting is too large or too small and effectively prevents the algorithm from falling into local optimum.The simulation result shows that the improved genetic algorithm can find the optimal query execution plan in a short time and improve the query efficiency.