Abstract
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.
Highlights
Genetic Algorithms (GAs) are stochastic search strategies based on natural selection mechanisms, which involve aspects of biological evolution to solve optimization problems
The model uses as optimization engine a modified GA called Pseudo Genetic Algorithm (PGA) that changes the binary coding of the chromosome for integer coding, this change has the advantage that each gene represents a Decision Variables (DVs) that in optimization of hydraulic problems gives good results [13] this algorithm is connected to the Storm Water Management Model (SWMM) [14] by a toolkit [15]
The inclusion of a Hydraulic Controls (HCs) in the optimization model significantly improves the results obtained
Summary
Genetic Algorithms (GAs) are stochastic search strategies based on natural selection mechanisms, which involve aspects of biological evolution to solve optimization problems One characteristic of these types of algorithms is the way they explore the solution space, while other algorithms follow a single search direction, GAs perform parallel searches in different directions. One of those that most worries researchers is the difficulty that GAs can present to find solutions close to the global optimum when they must explore a large search space This problem is called curse of dimensionality, it refers to the fact that when the Decision Variables (DVs) of a problem increase, the search space grows exponentially. Ndiritu and Daniell [9] presented an approach to decrease the search space by adaptively controlling the mapping of fixedlength chromosomes to real values, so that, in each subsequent iteration, the algorithm searches a Journal Name 2020, x, x; doi:10.3390/
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