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Proceedings Paper

Research on particle swarm optimization algorithm based on optimal movement probability
Author(s): Jianhong Ma; Han Zhang; Baofeng He
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Paper Abstract

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Paper Details

Date Published: 23 January 2017
PDF: 6 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103222A (23 January 2017); doi: 10.1117/12.2265257
Show Author Affiliations
Jianhong Ma, Zhengzhou Univ. (China)
Han Zhang, Zhengzhou Univ. (China)
Baofeng He, Sias International Univ. (China)
Zhengzhou Univ. (China)


Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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