Research of Query Optimization Based on Improved Quantum Particle Swarm Optimization Algorithm in Distributed Database

2012 ◽  
Vol 532-533 ◽  
pp. 1365-1369
Author(s):  
Fu Min Liu ◽  
Jing Yong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Query Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Distributed Database ◽  
Database Query ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Database query optimization is a very complicated issue, also is the key influencing factor in database systems performance. Database query operation efficiency is one of the key factors that affect system response time. Therefore, how to improve the efficiency of database query system becomes particularly important. This paper, on the basis of the advantages of Quantum particle swarm optimization algorithm, proposes distributed database query optimization methods based on Quantum particle swarm optimization algorithm, and improves algorithm. Simulation comparison experiments show that Quantum particle swarm optimization algorithm can improve the efficiency of the distributed database query, and is an effective way to solve the optimization of distributed database query.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

Quantum Particle Swarm Optimization Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 106-110 ◽  
Author(s):  
Yu Fa Xu ◽  
Jie Gao ◽  
Guo Chu Chen ◽  
Jin Shou Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Quantum Theory ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Experimental Results ◽  
Swarm Optimization ◽  
Local Optima ◽  
Quantum Particle Swarm Optimization

Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.

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