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You are here : eLibrary : IAHR World Congress Proceedings : 35th IAHR Congress - Chengdu (2013) : THEME 7 - WATER RESOURCES AND HYDROINFORMATICS : Solving Water Resources Allocation Problems Using Genetic Algorithms: A Hypothetical Case Study
Solving Water Resources Allocation Problems Using Genetic Algorithms: A Hypothetical Case Study
Author : Yanan Jiang, Adrian T. McDonald, Martin Clarke and Linda See
As a crucial resource, water is very important to society, socio-economic development and the environment. Yet this precious resource is unevenly distributed both spatially and temporally. Moreover, there is competition for water use between different water sectors. As the population continues to grow, water shortage is becoming an issue, and climate change may exacerbate the water shortage problem. Thus it is very important to find ways to realize better and more optimal water resource allocation (WRA). Modern WRA problems consist of large scale water transfers and infrastructures with highly complex objectives to achieve. Thus WRA problems are complex and nonlinear combinatorial problems where the number of candidate WRA plans increases exponentially with the size of the water supplies and water demand. This paper will develop a comprehensive WRA model. The model is quite complicated since it addresses a variety of issues. Moreover, the model considers both static and dynamic parameters. Since the developed WRA model is an NP hard problem, it is impossible to use conventional optimization techniques to solve it. Therefore, Genetic Algorithms (GAs), which are powerful optimization algorithms, were chosen to solve this problem. Normal GAs use normal crossover and mutation operators (genetic operators) which will generate a lot of infeasible solutions especially when dealing with WRA problems, which makes the GAs very inefficient and extremely time consuming some times. This paper adopted a spanning tree based GA which use new genetic operators according to the WRA problem itself and make sure no infeasible solutions generated. A hypothetical simple case study was solved to demonstrate the capability of the new GA. The results indicate that compared to normal GAs the performance can be improved greatly in terms of RAM usage, time consumption and computation efficiency. Overall, this preliminary study suggests that this modified GA provides quite a promising way forward to solving complex WRA problems with a large number of decision variables. The spanning tree based GA can achieve optimal or near optimal solutions with good accuracy. Moreover, there are several other advantages such as: the methods based on GAs can handle any form of objective function and any form of constraints; they exhibit stable convergence; they are easy to implement; and they have an acceptable computation time.
File Size : 390,176 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 35th IAHR Congress - Chengdu (2013)
Date Published : 19/07/2016
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