Distributed Database Query Based on Improved Genetic Algorithm

2016 ◽  
Author(s):  
ShaoHua Liu ◽  
Xing Xu
Keyword(s):  
Genetic Algorithm ◽  
Distributed Database ◽  
Improved Genetic Algorithm ◽  
Database Query

Research on Distributed Database Query Optimization Based on Genetic Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 2850-2853
Author(s):  
Yan Qin Li ◽  
Cai Tian Zhang
Keyword(s):  
Genetic Algorithm ◽  
Query Optimization ◽  
Optimization Algorithm ◽  
Distributed Storage ◽  
Cost Model ◽  
Distributed Database ◽  
Optimization Method ◽  
Performance Comparison ◽  
Database Query ◽  
Genetic Method

In order to improve the performance of the query optimization for the distribute database, an improved query optimization algorithm was proposed based on the genetic algorithm. The query execution cost model based on the genetic algorithm was proposed in this paper. The distributed database was emerged in the 70's of the last century and developed with the progress of the computer technology and network technology, the distributed database was the database system which is distributed storage dispersedly in physics and with centralized processing in mathematic logic. Because the storage points were not uniform, the structure of the distributed database is much more complicated than the centralized database. Both the genetic algorithm and the dynamic exhaustive planning algorithm were taken in the query simulation for the performance comparison. The result shows that the genetic query optimization method in this paper has better performance in the distributed database query application. The case study and the simulation result show that the algorithm can get a satisfactory optimization result in a few iterations and the query optimization algorithm based on the genetic method has nice performance of the query optimization property, and the consumption and costs of the query is reduced to the minimum. The method which this paper proposed has good application performance and is valuable to put into practice.

Application on Query of Distributed Database Based on Improved Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4617-4621
Author(s):  
Fu Xing Chen ◽  
Xu Sheng Xie
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Distributed Database ◽  
Improved Genetic Algorithm ◽  
Mutation Probability ◽  
Probabilistic Search ◽  
Crossover Probability ◽  
Crossover And Mutation

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater; In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly; In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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