Distributed Database Query Based on Improved Genetic Algorithm
Shaohua Liu, Xing Xu · 2016
In recent years, with the text, images, audio, video and other data doubled because of the rapid development of information technology, we have entered the era of big data. How to effectively analyze and use the data has been a hot research direction. So the research of distributed database came into being to adapt to this research requirement. Sometimes the traditional genetic algorithm can't generate the optimal query plan. Focusing on this defect of the traditional genetic algorithm, this paper presents an improved genetic search algorithm. The improved algorithm uses the FCM clustering algorithm to classify the data members firstly, and then set up the crossover and mutation probability for each category to solve the problem that the crossover and mutation rate is set too large or too small. Simulation results show that the improved algorithm can find the optimal query execution plan in a relatively short period of time and then improve the query efficiency.