基于混合群智能算法优化BP神经网络的粮食产量预测
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摘要
针对前馈神经网络预测粮食产量的方法易陷入局部最优的问题,提出一种基于粒子群算法和人工蜂群算法的改进BP神经网络模型.利用粒子群优化算法和人工蜂群算法在全局搜索能力上的不同优势,结合两者对BP神经网络的权值和阈值进一步优化,以提升粮食产量预测模型的准确性与鲁棒性.给出基于粒子群和人工蜂群混合的ABPSO算法的具体实现,并选择1979年至2012年我国粮食的产量及影响其产量的8项因素作为数据集进行试验.结果表明:改进的BP神经网络能够较好地预测国内近几年的粮食产量变化趋势;相比未优化的BP模型,新算法预测误差平均值由847 780 t降低至240 320 t,误差范围由1 894 200 t降低至586 800 t.
To solve the problem of feedforward neural network for predicting grain yield with easy falling into local optimum, an improved BP neural network model was proposed based on particle swarm optimization and artificial bee colony algorithm. According to the different advantages of particle swarm optimization algorithm and artificial bee colony algorithm in global search ability, the weight and the threshold of BP neural network were further optimized to improve the accuracy and robustness of grain yield prediction model. The specific implementation of artificial bee particle swarm optimization(ABPSO) algorithm was given based on particle swarm and artificial bee colony. The eight factors on Chinese grain yield and the yields from 1979 to 2012 were selected as data sets. The results show that the trend of grain yield in China in recent years can be better predicted by the improved BP neural network. Compared with unimproved BP model, the average prediction error of the new algorithm is decreased from 847 780 t to 240 320 t, and the error range is decreased from 1 894 200 t to 586 800 t.
To solve the problem of feedforward neural network for predicting grain yield with easy falling into local optimum, an improved BP neural network model was proposed based on particle swarm optimization and artificial bee colony algorithm. According to the different advantages of particle swarm optimization algorithm and artificial bee colony algorithm in global search ability, the weight and the threshold of BP neural network were further optimized to improve the accuracy and robustness of grain yield prediction model. The specific implementation of artificial bee particle swarm optimization(ABPSO) algorithm was given based on particle swarm and artificial bee colony. The eight factors on Chinese grain yield and the yields from 1979 to 2012 were selected as data sets. The results show that the trend of grain yield in China in recent years can be better predicted by the improved BP neural network. Compared with unimproved BP model, the average prediction error of the new algorithm is decreased from 847 780 t to 240 320 t, and the error range is decreased from 1 894 200 t to 586 800 t.
引文
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[18] GOLOV N, R?NNB?CK L. Big data normalization for massively parallel processing databases [J].Computer Standards and Interfaces,2017,54: 86-93.
[ 2 ] 杨可心,桑永胜.基于BP神经网络的DDoS攻击检测研究[J].四川大学学报(自然科学版),2017,54(1):71-75.YANG K X, SANG Y S. Research on DDoS detection based on BP neural network[J].Journal of Sichuan University (Natural Science Edition),2017,54(1):71-75.(in Chinese)
[ 3 ] 蔡润,武震,云欢,等.基于BP和SOM神经网络相结合的地震预测研究[J].四川大学学报(自然科学版),2018,55(2):307-315.CAI R,WU Z,YUN H, et al. Research on earthquake prediction based on BP and SOM neural network[J].Journal of Sichuan University (Natural Science Edition),2018,55(2):307-315.(in Chinese)
[ 4 ] OJHA V K, ABRAHAM A, SNá?EL V. Metaheuristic design of feedforward neural networks: a review of two decades of research [J]. Engineering Applications of Artificial Intelligence, 2017, 60:97-116.
[ 5 ] EBERHART R C, SHI Y. Particle swarm optimization: developments, applications and resources[C]//Proceedings of the IEEE Conference on Evolutionary Computation.Piscataway:IEEE, 2001:81-86.
[ 6 ] 何莉,王淼,李博.面向单目标优化的集成粒子群算法[J].重庆邮电大学学报(自然科学版),2017,29(4):527-534.HE L, WANG M, LI B. Ensemble particle swarm optimizer for single objective optimization[J].Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition),2017,29(4):527-534.(in Chinese)
[ 7 ] 易云飞,林郭隆,董文永.基于维度近邻关系扩散的改进粒子群优化算法[J].重庆邮电大学学报(自然科学版),2018,30(3):390-398.YI Y F, LIN G L, DONG W Y. Improved particle swarm optimization algorithm based on diffusion of dimension nearest neighbor relation[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition),2018,30(3):390-398.(in Chinese)
[ 8 ] KHALILI-DAMGHANI K, ABTAHI A, TAVANA M. A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems[J]. Reliability Engineering and System Safety, 2013, 111:58-75.
[ 9 ] 楼国红,张剑平.粒子群算法修正测距的无线传感器网络节点定位[J].吉林大学学报(理学版),2018,56(3):650-656.LOU G H, ZHANG J P. Ranging distance modified by particle swarm algorithm for WSN node localization[J]. Journal of Jilin University (Science Edition),2018,56(3):650-656.(in Chinese)
[10] JAIN N K, NANGIA U, JAIN J. A review of particle swarm optimization[J]. Journal of the Institution of Engineers (India): Series B, 2018,99(4):407-411.
[11] 秦全德,程适,李丽,等. 人工蜂群算法研究综述[J]. 智能系统学报, 2014, 9(2):127-135.QIN Q D, CHENG S, LI L,et al. Artificial bee colony algorithm: a survey[J].CAAI Transactions on Intelligent Systems,2014, 9(2):127-135.(in Chinese)
[12] BAKTASH N, MEYBODI M R. A new hybrid model of PSO and ABC algorithms for optimization in dynamic environment[J]. International Journal of Computer Theory and Engineering, 2012,4(3):362-364.
[13] EBERHART R C, SHI Y. Comparing inertia weights and constriction factors in particle swarm optimization[C]//Proceedings of the 2000 Congress on Evolutionary Computation. Piscataway:IEEE Computer Society, 2000:84-88.
[14] 周俊,陈璟华,刘国祥,等. 粒子群优化算法中惯性权重综述[J]. 广东电力, 2013, 26(7):6-12.ZHOU J, CHEN J H, LIU G X, et al. Summary on inertia weight in particle swarm optimization algorithm[J]. Guangdong Electric Power, 2013,26(7):6-12.(in Chinese)
[15] 李建平,宫耀华,赵思远,等.粒子群优化算法的改进及数值仿真[J].吉林大学学报(理学版),2017,55(2):322-332.LI J P, GONG Y H, ZHAO S Y, et al. Improvement of particle swarm optimization algorithm and numerical si-mulation[J]. Journal of Jilin University (Science Edition), 2017,55(2):322-332.(in Chinese)
[16] 刘三阳,张平,朱明敏.基于局部搜索的人工蜂群算法[J]. 控制与决策, 2014,29(1):123-128.LIU S Y, ZHANG P, ZHU M M. Artificial bee colony algorithm based on local search[J].Control and Decision,2014,29(1):123-128.(in Chinese)
[17] SMITH P, GREGORY P J. Climate change and sustai-nable food production[J]. Proceedings of the Nutrition Society, 2013, 72(1):21-28.
[18] GOLOV N, R?NNB?CK L. Big data normalization for massively parallel processing databases [J].Computer Standards and Interfaces,2017,54: 86-93.