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.
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.)
LI Jingming,NI Zhiwei,ZHU Xuhui等. Parallel Ensemble Learning Algorithm Based on Improved Binary Glowworm Swarm Optimization Algorithm and BP Neural Network[J]. , 2017, 30(2): 171-182.
[1] KENNEDY J, EBERHART R. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Washington, USA: IEEE, 1995: 1942-1948. [2] DORIGO M, BIRATTARI M. Ant Colony Optimization // SAMMUT C, WEBB G I, eds. Encyclopedia of Machine Leaning. New York, USA: Springer, 2010: 36-39. [3] TSAI P W, PAN J S, LIAO B Y, et al. Enhanced Artificial Bee Colony Optimization. International Journal of Innovative Computing Information & Control, 2009, 5(12): 5081-5092. [4] GAO X Z, WU Y, WANG X L. A Cultural Artificial Fish-Swarm Algorithm for Optimization. ICIC Express Letters(Applications), 2013, 4(1): 231-236. [5] KRISHNANAND K N, GHOSE D. Glowworm Swarm Based Optimi- zation Algorithm for Multimodal Functions with Collective Robotics Applications. Multiagent and Grid Systems, 2006, 2(3): 209-222. [6] KRISHNANAND K N, GHOSE D. Glowworm Swarm Optimisation: A New Method for Optimising Multi-modal Functions. International Journal of Computational Intelligence Studies, 2009, 1(1): 93-119. [7] BUSETTI F. Genetic Algorithm Overview[J/OL]. [2016-05-23]. https://zh.scribd.com/document/396655/Genetic-Algorithm-Overview. [8] SPEARS W M, DE JONG K A, BCK T, et al. An Overview of Evolutionary Computation // Proc of the European Conference on Machine Learning. London, UK: Spring-Verlag, 1993: 442-459. [9] 李祚泳, 易勇鸷. BP网络学习能力与泛化能力之间的定量关系式.电子学报, 2003, 31(9): 1341-1344. (LI Z Y, YI Y Z. Quantitative Relation between Learning Ability and Generalization Ability of BP Neural Network. Acta Electronica Sinica, 2003, 31(9): 1341-1344.) [10] SHI L, TANG X, L J H. PCA-Based PSO-BP Neural Network Optimization Algorithm // Proc of the 27th Chinese Control and Decision Conference. Washington, USA: IEEE, 2015: 1720-1725. [11] PAN X T, QU K Q. Wind Power Output Prediction with BP Neural Network Combining Genetic Algorithms // XU Q J, LI Y G, YANG X, eds. Advanced Materials Research. Pfffikon, Switzerland: TTP, 2014, 860/861/862/863: 2526-2529. [12] EBRAHIMI E, MONJEZI M, KHALESI M R, et al. Prediction and Optimization of Back-Break and Rock Fragmentation Using an Artificial Neural Network and a Bee Colony Algorithm. Bulletin of Engineering Geology & the Environment, 2016, 75(1): 27-36. [13] 王改革,郭立红,段 红.基于萤火虫算法优化BP神经网络的目标威胁估计.吉林大学学报(工学版), 2013, 43(4): 1064-1069. (WANG G G, GUO L H, DUAN H. et al. Target Threat Assessment Using Glowworm Swarm Optimization and BP Neural Network. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(4): 1064-1069.) [14] HOU Y, LI H Y. Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization // LIU D L, ZHU X B, XU K L, et al., eds. Applied Mechanics and Materials. Pfffikon, Switzerland: TTP, 2014, 513/514/515/516/517: 1096-1100. [15] 石 城.安徽省干旱灾害风险图研究.硕士学位论文.合肥:合肥工业大学, 2013. (SHI C. Research of Drought Disaster Risk Map for Anhui Province. Master Dissertation. Hefei, China: Hefei University of Technology, 2013.) [16] 王 胜,田 红,杨 玮,等.基于灾损的安徽冬小麦干旱灾害风险评估.中国农业大学学报, 2015, 20(1): 195-204. (WANG S, TIAN H, YANG W, et al. Drought Disaster Risk Assessment of Anhui Winter Wheat Based on the Damage. Journal of China Agricultural University, 2015, 20(1): 195-204.) [17] 苏 超,方 崇,黄伟军.SOM神经网络在农业旱情评价中的应用.人民黄河, 2011, 33(7): 93-95. (SU C, FANG C, HUANG W J. Application of SOM Neural Network to Comprehensive Evaluation of Agricultural Drought. The Yellow River, 2011, 33(7): 93-95.) [18] 徐立萍,张 鑫.基于BP神经网络的吉林省中部旱情预测.吉林水利, 2012(1): 40-41. (XU L P, ZHANG X. Drought Prediction for the Middle Part of Jilin Province Based on BP Neural Network. Jilin Water Resources, 2012(1): 40-41.) [19] KRAUSE J, CORDEIRO J, PARPINELLI R S, et al. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems // YANG X S, CUI Z H, XIAO R B, et al., eds. Swarm Intelligence and Bio-inspired Computation: Theory and Applications. Amsterdam, The Netherlands: Elsevier, 2013: 169-191. [20] LI M W, WANG X, GONG Y, et al. Binary Glowworm Swarm Optimization for Unit Commitment. Journal of Modern Power Systems and Clean Energy, 2014, 2(4): 357-365. [21] KENNEDY J, EBERHART R C. A Discrete Binary Version of the Particle Swarm Algorithm // Proc of the IEEE International Conference on Systems, Man, and Cybernetics. New York, USA: IEEE, 1997, V: 4104-4108. [22] NEZAMABADI-POUR H, ROSTAMI M, MAGHFOORI-FARSANGI M. Binary Particle Swarm Optimization: Challenges and New Solutions. Journal of Computing and Information Science in Engineering, 2008, 6(1): 21-32. [23] 倪志伟,张 琛,倪丽萍.基于萤火虫群优化算法的选择性集成雾霾天气预测方法.模式识别与人工智能, 2016, 29(2): 143-153. (NI Z W, ZHANG C, Ni L P. Haze Forecast Method of Selective Ensemble Based on Glowworm Swarm Optimization Algorithm. Pattern Recognition and Artificial Intelligence, 2016, 29(2): 143-153.) [24] 张文修,梁 怡.遗传算法的数学基础.第2版.西安:西安交通大学出版社, 2003. (ZHANG W X, LIANG Y. Mathematics Foundation of Genetic Algorithm. 2nd Edition. Xi′an, China: Xi′an Jiaotong University Press, 2003.)
2017, Vol. 30 
