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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