关键词: 数据库, 查询优化, 粒子群优化算法, 量子行为, 高斯变异

Abstract: Aiming at traditional quantum particle swarm algorithm in solving the database query optimization problems has slow convergence speed and premature convergence, a novel query optimization method of database based on Gauss Mutation Quantum behaved Particle Swarm Optimization algorithm（GM-QPSO）. Firstly, the mutation operator of the genetic algorithm is introduced into quantum particle swarm optimization algorithm to improve the global search ability, the particle position changes in a small range of the approximate optimal solution, and then it is applied to solve the query optimization problem of database, and the performance of GM-PSO is tested by simulation experiments. The results show that, GM-QPSO accelerates the convergence speed of database query optimization and can obtain higher quality query optimization scheme.

Key words: database, optimization query, particle swarm optimization algorithm, quantum behaved, Gauss mutation