simplified multi‐objective genetic algorithm optimization model for canal scheduling

A simplified Multi‐Objective Genetic Algorithm Optimization Model (MOM‐GA) for canal scheduling under unequal flow rates of distributary canals is presented in this paper. This MOM‐GA was designed for dynamic rotational scheduling with two objectives: to reduce fluctuations of flow rates of superior...

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Bibliographic Details
Published in:Irrigation and drainage Vol. 61; no. 3; pp. 294 - 305
Main Authors: Peng, S. Z, Wang, Y, Khan, S, Rana, T, Luo, Y. F
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01.07.2012
Subjects:
algorithms
canal scheduling
case studies
China
débits inégaux
genetic algorithm
irrigation canals
irrigation scheduling
multi-objectifs
multi-objective
non-linear
non-linéaires algorithme génétique
optimisation
optimization
programmation d'un canal
seepage
unequal flow rates
China
ISSN:1531-0353, 1531-0361
Online Access:Get full text
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Summary:A simplified Multi‐Objective Genetic Algorithm Optimization Model (MOM‐GA) for canal scheduling under unequal flow rates of distributary canals is presented in this paper. This MOM‐GA was designed for dynamic rotational scheduling with two objectives: to reduce fluctuations of flow rates of superior canals, and to reduce seepage losses of canal systems. This model was programmed in MATLAB using its genetic algorithm functions. Application of this model was demonstrated with a case study of the Nanguan Main Canal system (NMC) in the Gaoyou Irrigation Area, China. The results demonstrated that the MOM‐GA is an effective model for optimizing canal scheduling. NMC keeps running under a relatively steady range, and the seepage losses are reduced by around half that under current and binary optimized scheduling. The MOM‐GA is also sufficiently flexible to be applied to different levels in canal systems as a simplified approach for canal scheduling design and operation. The optimization results given by MOM‐GA can assist irrigators to make better canal scheduling decisions in each irrigation event.
Bibliography:http://dx.doi.org/10.1002/ird.654
Une version simplifiée d'un modèle d'optimisation utilisant un algorithme génétique multi-objectif pour la programmation d'un canal.
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ArticleID:IRD654
National Science Foundation of China - No. 50839002; No. 50809021
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1531-0353
1531-0361
DOI:10.1002/ird.654