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计算机系统应用英文版:2024,33(11):194-201
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基于VE-GEP算法的PM2.5浓度预测
(西安建筑科技大学 信息与控制工程学院, 西安 710055)
PM2.5 Concentration Prediction Based on VE-GEP Algorithm
(College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)
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Received:April 30, 2024    Revised:May 20, 2024
中文摘要: 准确预测PM2.5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PM2.5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PM2.5浓度预测研究提供了新的思路和方法.
Abstract:Accurate prediction of PM2.5 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 PM2.5 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 PM2.5 concentration prediction studies.
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基金项目: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)
引用文本:
王超学,邹飞.基于VE-GEP算法的PM2.5浓度预测.计算机系统应用,2024,33(11):194-201
WANG Chao-Xue,ZOU Fei.PM2.5 Concentration Prediction Based on VE-GEP Algorithm.COMPUTER SYSTEMS APPLICATIONS,2024,33(11):194-201