Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm

In a distributed database environment, multi-join query optimization is one of the key factors affecting database performance. Genetic algorithms have a good application in dealing with this type of problem. However, the traditional genetic algorithm has the problems of low efficiency and easily falls into the precocity when dealing with query optimization, which is mainly caused by the lack of population diversity. Therefore, this paper sets up a mathematical model for distributed database query optimization and proposes an adaptive genetic algorithm based on double entropy. We introduced a genetic algorithm with two types of entropy: genotype and phenotype. Genotype entropy was used to optimize the distribution of the initial population, ensuring that the initial population has good population diversity. Phenotype entropy is used to optimize the genetic strategy, which can be divided into individual entropy and population entropy. Individual entropy is used to optimize the selection strategy, and population entropy is used to optimize the crossover and mutation operators to maintain the population diversity in the iteration process and accelerate the speed of iteration. The experimental results show that the algorithm proposed in this paper is effective for query optimization of a distributed database.