# Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm
## 1 Introduction
Quantum computing has the ability to diversify and scale so that a larger query search space can be covered in the search. In this way, it speeds up convergence and helps to avoid falling into local optima. With such a strategy, the algorithm aims to determine an optimal order of connections to reduce the total execution time. Experimental results show that the convergence speed is higher using this method. However, for relatively small queries, it performs terribly and easy to precociy. [PERSON] 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. Genotype entropy is used to optimize the distribution of the initial population, ensuring a diverse set of initial solutions. Phenotype entropy, on the other hand, optimizes the genetic strategy and consists of individual entropy and population entropy. The algorithm improves the diversity of the population to prevent the algorithm from falling into local optima easily, but the algorithm lacks local search capability, which leads to slower convergence at a later stage. [PERSON] et al. [32] proposed a hybrid fuzzy ant colony optimization algorithm (FACO) for distributed database query optimization. The proposed FACO algorithm incorporates a fuzzy module that uses historical information to calculate the pheromone value and select the appropriate ACO parameters while maintaining optimal computing time. The algorithm sets different algorithm parameters for different input query statements by leveraging the advantages of ant colony optimization and fuzzy logic. This improves the query efficiency, but the use of a single search algorithm means that the method still has the problem of easily falling into local optimum. As the cluster size of distributed databases continues to expand, the data shardings and storage nodes will also increase, resulting in exacerbating the complexity of data query in distributed databases and further leading to the current query optimization algorithms suffering from problems of low optimization efficiency and easy to precocity in the face of high-dimensional join queries. Therefore, in this paper, a dynamic perturbation artificial bee colony algorithm combined with genetic operators is proposed to solve the distributed database problem. The main contributions of this paper are as follows:  
(1) A new artificial bee colony algorithm optimization strategy is used to solve the optimization problem of discrete feasible domains without the concept of distance;  
(2) A dynamic perturbation factor is proposed so that the parameters of the algorithm can change dynamically with the iterative process and the convergence degree of the whole population in order to improve the convergence efficiency of the algorithm;  
(3) The global search ability of the artificial bee colony algorithm is improved by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. The remainder of this paper is organized as follows: The design of the cost function and the proposed algorithm are expressed in Section 2. The data and results of the experiment are delivered in Section 3. Finally, Section 4 demonstrates conclusions and future work.