A Hybrid Optimization Method of Multi-objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) Classifier for Hydrological Model Calibration

机译：一种用于水文模型校准的多目标遗传算法（MOGA）和K最近邻（KNN）分类器的混合优化方法

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The MOGA is used as automatic calibration method for a wide range of water and environmental simulation models.The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real engineering problems. We propose a unique hybrid method of MOGA and KNN classifier to reduce the number of actual fitness evaluations. The test results for multi-objective calibration show that the proposed method only requires about 30% of actual fitness evaluations of the MOGA.

机译：MOGA被用作各种水和环境模拟模型的自动校准方法。估计整个Pareto集的任务需要在标准MOGA优化过程中需要大量的健身评估。然而，在许多真正的工程问题中获得目标函数的价值是非常耗时的。我们提出了一种独特的Moga和Knn分类器的混合方法，以减少实际健身评估的数量。多目标校准的测试结果表明，所提出的方法仅需要大约30％的MOGA实际健身评估。

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《The Intelligent Data Engineering and Automated Learning conference》|2004年||共6页
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Yang Liu; Soon-Thiam Khu; Dragon Savic;
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中图分类 TP3-532;
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4. A Hybrid Optimization Method of Multi-objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) Classifier for Hydrological Model Calibration [C] . Yang Liu, Soon-Thiam Khu, Dragon Savic International Conference on Intelligent Data Engineering and Automated Learning(IDEAL 2004); 20040825-20040827; Exeter; GB . 2004

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A Hybrid Optimization Method of Multi-objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) Classifier for Hydrological Model Calibration

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