Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

et al. 2015

Water Resour Manage

Self Cite

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking.A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm.The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.

Section: Wdn Design Based On Evolutionary Algorithmsmentioning

confidence: 99%

Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing

Mora-Meliá

et al. 2015

Water Resour Manage

Self Cite

“…Moreover, a minimum pressure at the nodes must be guaranteed. The objective function includes the capital costs for new pipes and penalty terms for minimum pressure violations [20]. Up-to-date best solutions are available from different authors [20,21,39,40].…”

Section: Methodsmentioning

confidence: 99%

“…Some examples are Genetic Algorithms [18][19][20], Harmony Search [21,22], Shuffled Complex Evolution [23], Shuffled Frog Leaping Algorithm [24], Particle Swarm Optimization [25], etc. In recent years, the development of methodologies to compare the performance of different algorithms has been of interest to the scientific community [26,27].…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of Setting Parameters in a Modified Shuffled Frog Leaping Algorithm Applied to Optimizing Water Distribution Networks

Mora-Meliá

et al. 2016

Water

This paper presents a modified Shuffled Frog Leaping Algorithm (SFLA) applied to the design of water distribution networks. Generally, one of the major disadvantages of the traditional SFLA is the high number of parameters that need to be calibrated for proper operation of the algorithm. A method for calibrating these parameters is presented and applied to the design of three benchmark medium-sized networks widely known in the literature (Hanoi, New York Tunnel, and GoYang). For each of the problems, over 35,000 simulations were conducted. Then, a statistical analysis was performed, and the relative importance of each of the parameters was analyzed to achieve the best possible configuration of the modified SFLA. The main conclusion from this study is that not all of the original SFL algorithm parameters are important. Thus, the fraction of frogs in the memeplex q can be eliminated, while the other parameters (number of evolutionary steps N s , number of memeplexes m, and number of frogs n) may be set to constant values that run optimally for all medium-sized networks. Furthermore, the modified acceleration parameter C becomes the key parameter in the calibration process, vastly improving the results provided by the original SFLA.

“…Because of the different nature of the variables, two algorithms have been used for network design: a pseudo-genetic algorithm (PGA) and the particle swarm optimization (PSO) algorithm. PGA is a modified genetic algorithm that replaces the binary coding of each variable by an integer coding [26]. Therefore, PGA is meant to address problems of discrete character.…”

Section: Applicationmentioning

confidence: 99%

Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization

Saldarriaga

et al. 2016

Water

Abstract:The Storm Water Management Model (SWMM) is a dynamic simulation engine of flow in sewer systems developed by the USEPA. It has been successfully used for analyzing and designing both storm water and waste water systems. However, despite including some interfacing functions, these functions are insufficient for certain simulations. This paper describes some new functions that have been added to the existing ones to form a library of functions (Toolkit). The Toolkit presented here will allow the direct modification of network data during simulation without the need to access the input file. To support the use of this library, a testing protocol was performed in order to evaluate both calculation time and accuracy of results. Finally, a case study is presented. In this application, this library will be used for the design of a sewerage network by using a genetic algorithm based on successive iterations.