Authors: XuGuang Zhu; Yuzhi Shen
Addresses: College of Innovation and Practice, Liaoning Technical University, Fuxin , 123000, China ' Liaoning Technical University, Fuxin , 123000, China
Abstract: MJQO problem is very complicated, query speed influences execution efficiency of database application software. To solve deficiencies such as low rate of convergence, etc., of PSO algorithm and improve optimisation efficiency of database multi-connection query, this thesis proposes a MJQO method adapting to escape momentum particle swarm optimisation aiming at deficiencies of particle swarm optimisation such as early-maturing, partial optimisation, etc., and it verifies effectiveness of SAEV-MPSO via emulation contrasted test, and this algorithm can obtain optimal query scheme of MJQO in relatively short-time. Crossover mechanism is first introduced by this algorithm of genetic algorithm to particle swarm algorithm to maintain diversity of it and prevent early-maturing phenomenon, and then this thesis introduces search track of momentum algorithm smoothness particle to accelerate convergence rate of particle swarm; finally this thesis applies this algorithm to database multi-connection query optimisation solution to achieve optimal database query scheme. Emulation result indicates this algorithm improves database query efficiency and shortens query response time.
Keywords: database query; archive information management; genetic algorithm; MapReduce; particle swarm.
International Journal of Reasoning-based Intelligent Systems, 2018 Vol.10 No.2, pp.117 - 121
Available online: 24 May 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article