Abstract
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.
Highlights
In the era of big data, in the face of increasing mass data, the disadvantages of traditional centralized database are increasingly appearing
We propose an adaptive double-entropy genetic algorithm (ADEGA)
The comparison algorithms selected in this paper were the adaptive genetic algorithm (AGA) [28] and the parallel ant colony algorithm (PACA) [29]
Summary
In the era of big data, in the face of increasing mass data, the disadvantages of traditional centralized database are increasingly appearing. Paper [11] proposed a query optimization method based on the Tabu-GEP algorithm, which combines the Tabu search strategy with the GEP algorithm It improves the performance of the classic GEP algorithm. Paper [12] proposed an adaptive genetic algorithm, which reintroduced individuals scattered outside the convergence part into genetic operations and adaptively adjusted the evolutionary strategy according to the different fitness of individuals to maintain the diversity of individuals It may introduce undesirable genes, which slows down the optimization process. During the process of evolution, we selected an appropriate evolutionary strategy according to population phenotype entropy to adaptively adjust the genetic operator and maintain individual diversity in the evolution process This can improve the global search ability of the entire algorithm and quickly obtain the optimal solution. It is almost impossible to search for the optimal QEP using an exhaustive method
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