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/).

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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  -