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A novel hybrid mechanistic-data-driven model identification framework using NSGA-n

机译：基于NSGA-n的新型混合动力数据驱动模型识别框架

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This paper describes a novel evolutionary data-driven model (DDM) identification framework using the NSGA-II multi-objective genetic algorithm. The central concept of this paper is the employment of evolutionary computation to search for model structures among a catalog of models, while honoring the physical principles and the constitutive theories commonly used to represent the system/ processes being modeled. The presented framework provides high computational efficiency through connecting a series of NSGA-ll runs which share results. Furthermore, the employment of a multi-objective optimization algorithm enables a unique way of incorporating different aspects of model goodness in the model selection process, and also, at the end of the search procedure, provides a number of potential optimal model structures, making it possible for the modeler to make a choice based on the goal of the modeling. As an illustration, the framework is used for modeling wash-off and build-up of suspended solids (TSS) in highway runoff. The performance of the discovered model confirms the potential of the proposed evolutionary DDM framework for modeling environmental processes.
机译：本文介绍了一种使用NSGA-II多目标遗传算法的新型进化数据驱动模型（DDM）识别框架。本文的中心概念是使用演化计算在模型目录中搜索模型结构，同时尊重通常用于表示要建模的系统/过程的物理原理和本构理论。提出的框架通过连接共享结果的一系列NSGA-11运行提供了很高的计算效率。此外，采用多目标优化算法可以在模型选择过程中采用独特的方式纳入模型优劣的不同方面，而且在搜索过程结束时，可以提供许多潜在的最优模型结构，从而使其建模人员可以根据建模目标做出选择。作为说明，该框架用于对高速公路径流中冲刷和悬浮固体（TSS）的堆积进行建模。发现的模型的性能证实了拟议的演化DDM框架对环境过程进行建模的潜力。

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