GPMBC-FZ: A Query Optimization Method in Distributed Database

The rapid growth in the scale of distributed databases has led to a deeper impact of the multi-join query optimization problem on data query efficiency. The increase in the number of relations causes a significant increase in the search space complexity of distributed database multi-join optimization, which greatly affects the efficiency of finding the optimal execution plan, thus hindering the further optimization of multi-join queries. In this regard, a genetic multiple bee colony algorithm combined with fuzzy logic is proposed to solve the problem of further optimizing multi-join queries of distributed databases. Firstly, the fast convergence property of the genetic algorithm is utilized to obtain a set of relatively optimal solutions in order to overcome the blindness of the artificial bee algorithm’s pre-search, and then the parallel multiple bee algorithm is introduced, which makes use of the information interaction between multiple bee colonies to better prevent the algorithm from falling into the local optimal situation as well as the parallelization of the execution of the multiple bee colonies to improve the search efficiency and convergence speed of the algorithm. Secondly, the fuzzy logic system is also used to optimize the parameters of the algorithm and set the corresponding algorithmic control parameters for different dimensional queries. Finally, a comparison experiment is conducted to test the average execution cost and algorithm execution time of the Top-K query plans output by the algorithm in the case of six query statements involving different lengths, and the experimental results show that the K optimal query plans generated by the method proposed in this paper have lower query excution cost and algorithm execution time, and can effectively improve the query efficiency.