摘要
针对分布式数据库多表查询速度慢的问题,提出一种改进的分布式数据库查询优化遗传算法。利用条件采样的方法,维持种群的多样性,防止算法陷入局部最优解;利用马氏链模型优化变异算子,确定变异算子当前状态下的最优取值,进行交叉和变异操作,找出最优查询执行计划。仿真结果表明,改进算法能在较短时间内找到最优的查询执行计划,加快查询速度,提高查询效率。
An improved query optimization method based on genetic algorithm is proposed to solve the problem of the slow multi-table query speed of distributed database.A conditional sampling method is used to maintain the diversity of popula-tion in case it traps into local optima.The mutation operator is optimized by using Markov-chain model to decide its optimal value under the current state,then crossover and mutation operator is proceeded to find out the optimal query execution plan.Simulation results show that the proposed algorithm can find the optimal query execution plan in a short time.It can speed up the query process and improve the efficiency of query.
出处
《桂林电子科技大学学报》
2015年第3期217-221,共5页
Journal of Guilin University of Electronic Technology
基金
广西科学研究与技术开发计划(桂科攻14124005-2-9)
桂林电子科技大学研究生教育创新计划(XY130218)
关键词
分布式数据库
查询优化
马氏链模型
遗传算法
distributed database
query optimization
Markov-chain model
genetic algorithm
