基于GM-QPSO算法的数据库查询优化

针对量子粒子群算法解决数据库查询优化问题存在缺陷,提出一种高斯变异量子粒子群算法的数据库查询优化方法(GM-QPSO)。首先将遗传算法的变异算子引进量子粒子群优化算法,使得粒子在近似最优解附近变动提高全局搜索能力,然后将其应用于数据库查询优化问题求解,最后通过仿真实验对GM-QPSO的性能进行测试。结果表明,GM-QPSO加快了数据库查询优化求解的收敛速度,获得了质量更高的查询优化方案。

Database query optimization based on GM-QPSO algorithm

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.