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

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.

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

“…Figure 19 shows the execution time of each algorithm. In Figures 18 and 19, MACGA is the multi-ant colony genetic algorithm [29], and DEGA is the adaptive genetic algorithm based on double entropy [31]. As the figures show, compared to the other two methods, the proposed method in this paper is able to find less costly execution plans for queries of different lengths and with the same iteration limitations.…”

“…Zheng et al [31] proposed an adaptive genetic algorithm based on double entropy for distributed database query optimization. The two types of entropy are genotype entropy and phenotype entropy.…”

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“…The adaptability of advanced query optimization methods to different data types and query complexities was a recurring theme in both qualitative and quantitative findings. This versatility is particularly important in big data environments, where the diversity of data and queries poses significant optimization challenges (Zheng et al, 2022). The ability to apply these techniques across various scenarios without extensive customization contrasts with traditional methods that often require tailored solutions for specific use cases.…”

Advanced Query Optimization in SQL Databases for Real-Time Big Data Analytics

“…The adaptability of advanced query optimization methods to different data types and query complexities was a recurring theme in both qualitative and quantitative findings. This versatility is particularly important in big data environments, where the diversity of data and queries poses significant optimization challenges (Zheng et al, 2022). The ability to apply these techniques across various scenarios without extensive customization contrasts with traditional methods that often require tailored solutions for specific use cases.…”