Optimization of water distribution networks using genetic algorithm

Güç, Gerçek
This study gives a description about the development of a computer model, RealPipe, which relates genetic algorithm (GA) to the well known problem of least-cost design of water distribution network. GA methodology is an evolutionary process, basically imitating evolution process of nature. GA is essentially an efficient search method basically for nonlinear optimization cases. The genetic operations take place within the population of chromosomes. By means of various operators, the genetic knowledge in chromosomes change continuously and the success of the population progressively increases as a result of these operations. GA optimization is also well suited for optimization of water distribution systems, especially large and complex systems. The primary objective of this study is optimization of a water distribution network by GA. GA operations are realized on a special program developed by the author called RealPipe. RealPipe optimizes given water network distribution systems by considering capital cost of pipes only. Five operators are involved in the program algorithm. These operators are generation, selection, elitism, crossover and mutation. Optimum population size is found to be between 30-70 depending on the size of the network (i.e. pipe number) and number of commercially available pipe size. Elitism rate should be around 10 percent. Mutation rate should be selected around 1-5 percent depending again on the size of the network. Multipoint crossover and higher rates are advisable. Also pressure penalty parameters are found to be much important than velocity parameters. Below pressure penalty parameter is the most important one and should be roughly 100 times higher than the other. Two known networks of the literature are examined using RealPipe and expected results are achieved. N8.3 network which is located in the northern side of Ankara is the case study.


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Citation Formats
G. Güç, “Optimization of water distribution networks using genetic algorithm,” M.S. - Master of Science, Middle East Technical University, 2006.