Mass Data Query Optimization Based on Multi-objective Co-evolutionary Algorithm
- DOI
- 10.2991/amcce-17.2017.168How to use a DOI?
- Keywords
- mass data; query optimization; evolutionary algorithm; multi-objective; cooperative computing
- Abstract
multi-connection database query optimization belongs to a kind of typical complex problem and cost of optimal query strategy obtained from traditional Particle Swarm Optimization Algorithm is relatively high under some conditions and it is easy to fall into local optimal solution. Based on Quantum Particle Swarm Optimization Algorithm, the paper puts forward a kind of optimal algorithm for database query, namely, mass data query algorithm based on multi-objective co-evolutionary algorithm to improve optimization efficiency of database query and optimize performance of algorithm of the paper in solution of database query optimization problems by simulation experiment. The paper puts forward a kind of Gaussian Mutation Quantum Particle Swarm Optimization Algorithm and introduces Gaussian mutation to avoid prematurity phenomenon. Experimental result shows that algorithm of the paper can obtain more optimized query effect when solving multi-list connection database query optimization problems.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ting Zhang PY - 2017/03 DA - 2017/03 TI - Mass Data Query Optimization Based on Multi-objective Co-evolutionary Algorithm BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 952 EP - 957 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.168 DO - 10.2991/amcce-17.2017.168 ID - Zhang2017/03 ER -