Application Research Of Improved Genetic And Ant Colony Algorithm In Database Query Optimization

With the development of information society,database technology has become an important technical means in organizing and managing data.In all operations of database,query operation is the most frequently used.As a very important operation,query plays an important role in the process of processing a large amount of information.Based on the theory of database,this paper studies the query algorithm in theory and literature.The genetic algorithm has the characteristics of universality,universality and fast convergence,but the disadvantage of the algorithm is that the probability of local optimal solution is high.In seeking the best solution,the ant colony algorithm achieves good results.But in the early stage of searching path,there are only a few pheromones and the algorithm lacks the guidance of pheromones,ant selection path has a certain degree of blindness and randomness,which results in the algorithm consuming a lot of time to get the optimal solution.The hybrid genetic ant colony algorithm guarantees fast convergence and improves the performance of finding solutions.On this basis,how to improve the conventional genetic ant colony hybrid algorithm to further improve the efficiency of database multi-join query optimization has become the focus of this paper.The following situations are studied in this paper.we study the population diversity and analyze the selection operator.In order to avoid the decrease of selection efficiency with the decrease of diversity in the process of algorithm evolution,an adaptive selection strategy is proposed.This paper studies population diversity and analyses the influence of population diversity on selection operator.In order to avoid the decrease of selection efficiency with the decrease of population diversity in the process of algorithm evolution,an adaptive selection strategy is proposed to improve the efficiency of selection operator and increase the possibility of complex descendants.Because of the randomness of the crossover position,the algorithm is prone to invalid crossover,which reduces the algorithm's efficiency.In this paper,the principle of two-point crossover is analyzed,and the crossover operation is improved according to the principle of correlation.In order to avoid missing the optimal convergence time due to blindly fixed iteration times,this paper judges the convergence state of genetic algorithm according to the changing trend of population diversity in the process of algorithm evolution,and adopts an adaptive method for the convergence of hybrid algorithm.In the process of algorithm,it is easy to find the phenomenon that the optimal individual does not evolve effectively and converges locally.In this paper,the population iteration method is improved to reduce the probability of the optimal individuals being filtered.In summary,this paper improves the genetic ant colony algorithm from the aspects of selection strategy,crossover operation,algorithm convergence point and offspring population.Experiments show that the improved algorithm improves the probability of the optimal solution to 17%,and the execution time and convergence algebra are shortened.