基于VE-GEP算法的PM_(2.5)浓度预测  

PM_(2.5) Concentration Prediction Based on VE-GEP Algorithm

作　　者：王超学[1] 邹飞 WANG Chao-Xue;ZOU Fei(College of Information and Control Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学信息与控制工程学院,西安710055

出　　处：《计算机系统应用》2024年第11期194-201,共8页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(62072363);陕西省自然科学基础研究计划面上项目(2019JM-167)。

摘　　要：准确预测PM_(2.5)浓度对于公众健康和环境保护具有重要意义,但其非线性、多变性以及复杂性的特点导致难以准确预测.基于此,本文针对传统GEP存在的不足,提出了一种基于病毒进化的基因表达式编程算法(VE-GEP)来预测PM_(2.5)浓度.该算法在GEP的基础上引入了复活机制与诱变重启机制.复活机制能去除种群中的劣质个体,改善种群中个体的质量;诱变重启机制通过引入优质基因和新的个体,提高种群的多样性,增强算法的寻优能力.实验结果表明, VE-GEP算法相较于GEP、DSCE-GEP和CNN-LSTM在春季、夏季和秋季中的预测模型均有不同程度的提高,拟合度分别提高1.28%/0.1%/0.13%、1.86%/1.29%/0.42%、0.57%/0.24%/0.29%,为PM_(2.5)浓度预测研究提供了新的思路和方法.Accurate prediction of PM_(2.5) concentration is essential for public health and environmental protection,but its nonlinearity,variability,and complexity make it difficult.Based on this,this study proposes a gene expression programming algorithm based on virus evolution(VE-GEP)to predict PM_(2.5) concentration in response to the shortcomings of traditional GEP.The algorithm introduces a resurrection mechanism and a mutagenic restart mechanism based on GEP.The resurrection mechanism removes poor-quality individuals from the population and improves individual quality in the population.The mutagenic restart mechanism increases population diversity and enhances algorithm optimization-seeking ability by introducing high-quality genes and new individuals.Experimental results show that the VE-GEP algorithm improves the prediction models to different degrees compared to GEP,DSCE-GEP,and CNN-LSTM in spring,summer,and fall,with improvements in the fitness of 1.28%/0.1%/0.13%,1.86%/1.29%/0.42%,and 0.57%/0.24%/0.29%,respectively,which provides new ideas and methods for PM_(2.5) concentration prediction studies.

关 键 词：基因表达式编程 复活机制 诱变重启机制 病毒进化 PM_(2.5)浓度预测 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] X513[自动化与计算机技术—控制科学与工程]