计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
8期
103-107
,共5页
数据库%查询优化%粒子群优化算法%量子行为%高斯变异
數據庫%查詢優化%粒子群優化算法%量子行為%高斯變異
수거고%사순우화%입자군우화산법%양자행위%고사변이
database%optimization query%particle swarm optimization algorithm%quantum behaved%Gauss mutation
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。
針對量子粒子群算法解決數據庫查詢優化問題存在缺陷,提齣一種高斯變異量子粒子群算法的數據庫查詢優化方法(GM-QPSO)。首先將遺傳算法的變異算子引進量子粒子群優化算法,使得粒子在近似最優解附近變動提高全跼搜索能力,然後將其應用于數據庫查詢優化問題求解,最後通過倣真實驗對GM-QPSO的性能進行測試。結果錶明,GM-QPSO加快瞭數據庫查詢優化求解的收斂速度,穫得瞭質量更高的查詢優化方案。
침대양자입자군산법해결수거고사순우화문제존재결함,제출일충고사변이양자입자군산법적수거고사순우화방법(GM-QPSO)。수선장유전산법적변이산자인진양자입자군우화산법,사득입자재근사최우해부근변동제고전국수색능력,연후장기응용우수거고사순우화문제구해,최후통과방진실험대GM-QPSO적성능진행측시。결과표명,GM-QPSO가쾌료수거고사순우화구해적수렴속도,획득료질량경고적사순우화방안。
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.