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Parallel Ensemble Learning Algorithm Based on Improved Binary Glowworm
Swarm Optimization Algorithm and BP Neural Network |
| LI Jingming1,2, NI Zhiwei1, ZHU Xuhui1 , XU Ying3 |
1.School of Management, Hefei University of Technology, Hefei 230009
2.Institute of Information Engineering, Anhui Xinhua University, Hefei 230088
3.Key Laboratory of Atmospheric Science and Satellite Remote Sensing of Anhui Province,Meteorological Science Institute of Anhui Province, Hefei 230001 |
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Abstract The traditional back propagation(BP) neural network has low learning speed and calculution accuracy and it is easy to fall into local solution. Aiming at these defects, a parallel ensemble learning algorithm based on improved binary glowworm swarm optimization algorithm(IBGSO) and BP neural network is proposed. Firstly, a kind of improved binary glowworm swarm algorithm is constructed based on Gauss variation function as probability mapping function, and the validity of the algorithm is analyzed theoretically. Secondly, The IBGSO algorithm and BP neural network are combined to construct a parallel ensemble learning algorithm. Finally, the parallel ensemble learning algorithm is applied to the assessment of agricultural drought disaster. The experimental results show that the algorithm has advantages over the traditional algorithms in terms of convergence speed and operation accuracy. Therefore, IBGSO-BP algorithm can effectively improve the accuracy of agricultural drought assessment.
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| Fund:Supported by National High Technology Research Program of China(863 Program)(No.2015AA042101), National Natural Science Foundation of China(No.91546108,71271071), Key Project of Natural Science Research of Anhui Provincial Department of Education(No.KJ2016A308) |
| About author:: (LI Jingming(Corresponding author), born in 1978, Ph.D. candidate. His research interests include intelligent computing, data mining and machine learning.)(NI Zhiwei, born in 1963, Ph.D., professor. His research interests include data mining, machine learning and artificial intelligence.)(ZHU Xuhui, born in 1990, Ph.D. candidate. His research interests include intelligent computing, data mining and machine learning.)(XU Ying, born in 1979, master, senior engineer. Her research interests include meteorological disaster assessment and prediction, disaster loss assessment.) |
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2017, Vol. 30 
