• 论文 •

基于改进萤火虫算法的SVR空气污染物浓度预测模型

1. 1. 合肥工业大学管理学院, 合肥 230009; 2. 阜阳师范大学信息工程学院, 阜阳 236041; 3. 合肥工业大学过程优化与智能决策教育部重点实验室,合肥 230009
• 出版日期:2020-06-25 发布日期:2020-08-25

LI Ping,NI Zhiwei,ZHU Xuhui, SONG Juan. Air Pollutant Concentration Forecast Model of SVR Based on Improved Glowworm Swarm Optimization Algorithm[J]. Journal of Systems Science and Mathematical Sciences, 2020, 40(6): 1020-1036.

Air Pollutant Concentration Forecast Model of SVR Based on Improved Glowworm Swarm Optimization Algorithm

LI Ping 1,2,3,NI Zhiwei 1,3,ZHU Xuhui 1,3, SONG Juan 1,3

1. 1. School of Management, Hefei University of Technology, Hefei 230009; 2. School of Information Engineering, Fuyang Normal University,Fuyang  236041, 3.  Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei  230009
• Online:2020-06-25 Published:2020-08-25

In order to predict the air pollutant concentration accurately, air pollutant concentration forecast model of support vector regression (SVR) based on improved glowworm swarm optimization (IGSO) algorithm is proposed. Firstly, the glowworm swarm optimization algorithm is improved by using good point set, a swarm degree and varied step. Secondly, the training set is constructed according to the time series data of air pollutant concentration, and the optimal parameters of SVR are searched by IGSO algorithm. Finally, the air pollutant concentration is forecast by the SVR with the optimal parameters. Experiments in two parts show the performance of the proposed method. 1) Repeated experiments on 8 benchmark global optimization problems show that IGSO algorithm can find better objective function values with smaller variance than GSO algorithms based on other improved strategies.The experiments demonstrate that the IGSO algorithm has good performance on stability and computational accuracy. 2) The results of experiments on air pollutant concentration in three cities of Jing-Jin-Ji region are as follows. First of all, compared with the glowworm swarm optimization algorithm, particle swarm optimization algorithm and genetic algorithm, the multiple optimization results of SVR parameters based on IGSO are less volatile, and the cross validation error of SVR model and its variance are smaller. In addition, the proposed method has higher prediction accuracy for the test sets than SVR models based on other optimization algorithms mentioned above, SVR model based on grid search and BP neural network. Therefore, the air pollutant concentration forecast model of SVR based on IGSO algorithm (IGSOSVR) has higher stability and prediction accuracy.

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