Parallel Ensemble Learning Model Based on Hybrid Improved GSO and GRNN
JIAN Shuqiang1,2, NI Zhiwei1,2, LI Jingming3, ZHU Xuhui1,2, NI Liping1,2
1.School of Management, Hefei University of Technology, Hefei 230009 2.Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009 3.School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030 Citation
Abstract Glowworm swarm optimization(GSO) has problems of instability and slow convergence speed and accuracy. The error of general regression neural network(GRNN) is easily caused by the network structure. Aiming at these defects, a parallel ensemble learning model based on hybrid improved GSO and GRNN is proposed, and it is applied for haze prediction. Firstly, a hybrid improved glowworm swarm optimization(HIGSO) algorithm is constructed fusing multiple search strategies. The performance of the algorithm is verified via standard test functions. Next, the HIGSO algorithm is integrated with GRNN with disturbance parameter to construct a parallel ensemble learning model, and its validity and feasibility are verified on UCI standard dataset. Finally, the proposed model is applied for haze prediction in Beijing, Shanghai and Guangzhou areas to further verify its performance in haze prediction.
Fund:Supported by National Natural Science Foundation of China(No.91546108,71521001,71490725,71301041), National Natural Science Foundation of Anhui Province(No.1708085MG169)
Corresponding Authors:
NI Zhiwei .Ph.D., professor. His research interests include artificial intelligence, machine learning and cloud computing.
About author:: (JIAN Shuqiang, master student. His research interests include machine learning and data mining.) (LI Jingming, Ph.D., lecturer. His research interests include intelligent computing and data mining.) (ZHU Xuhui, Ph.D., lecturer. His research interests include intelligent computing and machine learning.) (NI Liping, Ph.D., associate professor. Her research interests include machine learning and data mining.)
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2019, Vol. 32 
