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
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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2017, Vol. 30 
