基于杂交变异粒子群优化算法的数据库查询优化  

Query Optimization of Database Based on Hybrid Variable Particle Swarm Optimization Algorithm

作　　者：陈芬[1] 唐彦[2] 

机构地区：[1]宿迁学院信息工程学院,江苏宿迁223800 [2]河海大学计算机与信息学院,江苏南京210024

出　　处：《淮海工学院学报（自然科学版）》2016年第4期8-11,共4页Journal of Huaihai Institute of Technology:Natural Sciences Edition

基　　金：国家自然科学基金资助项目(61103017);中央高校基本科研业务费专项资金资助项目(2013B02014)

摘　　要：针对传统粒子群优化算法存在的易陷入局部最优和早熟收敛问题,为了提高数据库查询的优化效率,提出一种杂交变异粒子群算法的数据库查询优化方法(HV-PSO).首先建立数据库查询优化数学模型;然后采用粒子间的信息传递和共享机制,找到最优计划,在寻优过程中创新引入动态惯性权重和"杂交变异"算子,增加种群多样性;最后采用仿真实验测试算法的性能.仿真结果表明,HV-PSO较好地解决了传统粒子群优化算法存在的不足,不仅提高了数据库查询效率,而且可以获得更加理想的数据库查询计划,尤其对连接关系数目较大的查询问题优势更加明显.Aiming at solving the problem of premature convergence and local optimal trap in the traditional particle swarm optimization algorithm,this paper proposed a novel database query optimization algorithm called hybrid variable particle swarm optimization（HV-PSO）.Firstly,database query optimization mathematical model is established,and the optimal solution is found by using information transferring and sharing mechanism of particles.This research has two novelties which contribute to the literature,namely,dynamic charge of particle inertia weight in the optimization process to accelerate the convergence and the introduction of＂hybrid variation＂operator to increase the diversity of population.Finally,the simulation experiments are carried out to test the performance of HV-PSO.The results show that the HV-PSO could solve the deficiency of the traditional particle swarm optimization algorithm,not only improving the database query efficiency,but also obtaining better query plan.Especially,it has predominant advantage for querying large relational connections.

关 键 词：数据库查询 粒子群算法 动态惯性权重 多连接查询 杂交变异 查询计划 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]