Using Genetic Algorithms for the optimization of engineering problems has gained popularity within the water resources’ research community. These algorithms based on some biological mechanisms demonstrate robustness and efficiency in finding solutions. However, one of the problems faced by this type of heuristic approach is that the efficiency of the algorithm decreases when applied to real-world problems due to the large space that it must explore to find an optimal solution. For this reason, it is necessary to limit the space that the algorithm must explore to the most promising regions.
This article presents a methodology to rehabilitate urban drainage networks using an iterative procedure to reduce the solutions searching space. The procedure is based on shorten the initially wide search space to one that contains the optimal solution. Through iterative processes, the search space is gradually reduced to define the final region that contains the optimal solution. Once this region is established, a finer discretization is used in the exploration of the space to find the optimal solution. The optimization process includes the replacement of pipelines and the incorporation of storm tanks and hydraulic controls into the network. To achieve this, an optimization model has been developed that uses a Genetic Algorithm as an optimization engine connected to the Storm Water Management Model (SWMM) through a toolkit. The methodology also contemplates the adjustment of certain algorithm operators to improve their efficiency. Finally, this methodology is applied to a real drainage network that needs to be rehabilitated. The obtained results demonstrate the effectiveness of the process of reducing the space for search solutions to face these types of problems.
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