Database Multi-Connection Query Optimization Based on Improved Snake Optimization Algorithm

In the face of a large amount of data and complex multi-connection query requirements, Finding a quick way to search for the best query execution plan is a research hotspot in improving database query speed. This paper proposes a database multi-connection query optimization method based on the improved snake optimization algorithm, An iterative chaotic mapping is utilized to initialize the population and enhance the population's diversity; A multi-armed bandit strategy is employed for the dynamic selection of population behavior modes; furthermore, an opposites-learning strategy is employed to update the optimal solution and population acquired at each iteration, thereby enhancing the algorithm's self-learning capability. Experimental results show that the algorithm proposed in this paper can find a better query execution plan in a shorter time.