自适应逃逸动量粒子群算法的数据库多连接查询优化

Multi-join query optimization of database based on self-adaptive escape velocity momentum particle swarm optimization algorithm

为了提高数据库多连接查询的优化效率,针对粒子群算法存在的早熟、局部最优等缺陷,提出一种自适应逃逸动量粒子群算法的数据库多连接查询优化方法.该算法首先将遗传算法的交叉机制引入粒子群算法中,以保持粒子群的多样性,避免早熟现象出现;然后,引入动量算法平滑粒子搜索轨迹,加快粒子群的收敛速度;最后,将该算法应用于数据库多连接查询优化求解,以获得最优的数据库多连接查询方案.仿真结果表明,该算法提高了数据库查询效率,缩短了查询响应时间.

In order to improve the multi-join query optimization etticiency, this paper proposeci a multtjoin query optimization of database based on self-adaptive escape velocity particle swarm optimization algorithm, which because the particle swarm optimization algorithm has premature, Local optimum etc defects. Firstly, the crossover mechanism of genetic algorithm is introduced into PSO to keep the diversity of particle swarm and avoid premature phenomenon. And then the momentum algorithm is introduced to smooth the trajectory of particle search and speed up the convergence. Finally, this algorithm is applied to solve multi-join query optimization of database to obtain the optimal solution. The simulation results show that this proposed algorithm improves the query efficiency, reduce the query response time.