A hybrid optimization method of Multi-Objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) classifier for hydrological model calibration

Yang Liu*, Soon Thiam Khu, Dragon Savic

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsZheng Rong Yang, Richard Everson, Hujun Yin
PublisherSpringer Verlag
Pages546-551
Number of pages6
ISBN (Print)3540228810, 9783540228813
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Dive into the research topics of 'A hybrid optimization method of Multi-Objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) classifier for hydrological model calibration'. Together they form a unique fingerprint.

Cite this