TY  - JOUR
T1  - Mixing Evolutionary Algorithms by Data Mining Approach for Query Optimization
AU - Sharif, Saeed AU - Reza Ghaffary, Hamid 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 12
IS  - 6
SP  - 128
EP  - 136
PY  - 2019
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2019.128.136
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2019.128.136
KW  - Query optimization
KW  -genetic operator
KW  -PSO algorithm
KW  -parallel
KW  -database
AB  - Optimizing database queries is one of the
problems with research issues. Comprehensive search
methods such as dynamic programming is suitable for
queries with a few relations but by increasing the number
of existing relations in the query, due to the need to use a
lot of memory and processing, use of these methods will
not be suitable, so we have to use the accidental and
evolutionary methods. Using evolutionary methods due to
their performance and strengthen, has become a suitable
research area in the field of query optimization. In this
study, a parallel hybrid evolutionary algorithm is
proposed for solving order optimization problem of
running join operators in the database queries. The
algorithm uses two methods of genetic algorithms and
learning automata for searching in the problem states
space at the same time. In this study, it is shown that by
using a synchronously Particle Swarm Optimization
algorithm (PSO) in parallel with the genetics crossover
operator in the search process, the speed of receiving
answer increases and it is prevented from algorithms to
get stuck in the local minimums. The practical results of
this study show that the hybrid algorithm shows superior
to methods based on other algorithms.
ER  -