Routing and flow control by genetic algorithm for a flow model

2002 ◽  
Vol 34 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Koji Okuhara ◽  
Toshijiro Tanaka ◽  
Hiroaki Ishii
Keyword(s):  
Genetic Algorithm ◽  
Flow Control ◽  
Flow Model

Genetic Algorithm Optimized Power Flow Control with Facts Devices

2013 ◽  
Vol 1 (4) ◽  
pp. 17-27
Author(s):  
G.N. Sreenivas ◽  
◽  
A. Yashoda Devi ◽  
K. Suresh Kumar ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Flow Control ◽  
Power Flow ◽  
Power Flow Control ◽  
Facts Devices

Optimization of Aifoil Flow Control Using a Genetic Algorithm with Diversity Control

2007 ◽  
Vol 44 (4) ◽  
pp. 1337-1349 ◽  
Author(s):  
Liang Huang ◽  
George Huang ◽  
Raymond LeBeau ◽  
Thomas Hauser
Keyword(s):  
Genetic Algorithm ◽  
Flow Control ◽  
Diversity Control

scholarly journals Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

2017 ◽  
Vol 20 (2) ◽  
pp. 440-456
Author(s):  
J. Drisya ◽  
D. Sathish Kumar
Keyword(s):  
Genetic Algorithm ◽  
Flow Model ◽  
Overland Flow ◽  
Fitness Function ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
Automatic Data ◽  
Automated Calibration ◽  
Manning’S Roughness Coefficient ◽  
Overland Flow Model

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

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