scholarly journals Use of Energy-Based Domain Knowledge as Feedback to Evolutionary Algorithms for the Optimization of Water Distribution Networks

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3101
Author(s):  
Diego Páez ◽  
Camilo Salcedo ◽  
Alexander Garzón ◽  
María Alejandra González ◽  
Juan Saldarriaga
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Domain Knowledge ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Nsga Ii ◽  
Acceptable Solution ◽  
Capital Costs ◽  
Periodic Feedback

The optimization of water distribution networks (WDN) has evolved, requiring approaches that seek to reduce capital costs and maximize the reliability of the system simultaneously. Hence, several evolutionary algorithms, such as the non-dominated sorting-based multi-objective evolutionary algorithm (NSGA-II), have been widely used despite the high computational costs required to achieve an acceptable solution. Alternatively, energy-based methods have been used to reach near-optimal solutions with reduced computational requirements. This paper presents a method to combine the domain knowledge given by energy-based methods with an evolutionary algorithm, in a way that improves the convergence rate and reduces the overall computational requirements to find near-optimal Pareto fronts (PFs). This method is divided into three steps: parameters calibration, preprocessing of the optimal power use surface (OPUS) results, and periodic feedback using OPUS in NSGA II. The method was tested in four benchmark networks with different characteristics, seeking to minimize the costs of the WDN and maximizing its reliability. Then the results were compared with a generic implementation of NSGA-II, and the performance and quality of the solutions were evaluated using two metrics: hypervolume (HV) and modified inverted generational distance (IGD+). The results showed that the feedback procedure increases the efficiency of the algorithm, particularly the first time the algorithm is retrofitted.

scholarly journals Placing an ensemble of pressure sensors for leak detection in water distribution networks under measurement uncertainty

2018 ◽  
Vol 21 (2) ◽  
pp. 223-239 ◽  
Author(s):  
Ehsan Raei ◽  
M. Ehsan Shafiee ◽  
Mohammad Reza Nikoo ◽  
Emily Berglund
Keyword(s):  
Measurement Uncertainty ◽  
Water Distribution ◽  
Water Pressure ◽  
Pressure Sensors ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Nsga Ii ◽  
New Approach ◽  
Trade Offs ◽  
Selection Of

Abstract Large volumes of water are wasted through leakage in water distribution networks, and early detection of leakages is important to minimize lost water. Pressure sensors can be placed in a network to detect changes in pressure that indicate the presence of a new leak. This study presents a new approach for placing a set of pressure sensors by creating a list of candidate locations based on sensitivity to leaks that are simulated at all potential nodes in a network. The selection of a set of sensors is explored for two objectives, which are the minimization of the number of sensors and the time of detection. The non-dominated sorting genetic algorithm (NSGA-II) is used to explore trade-offs between these objectives. The effect of measurement uncertainty on the selection of sensor locations is explored by identifying alternative non-dominated fronts for different values for sensor error. The evolutionary algorithm-based approach is applied and demonstrated for the C-Town water network.

scholarly journals Impact of diffusion and dispersion of contaminants in water distribution networks modelling and monitoring

2019 ◽  
Vol 20 (1) ◽  
pp. 46-58 ◽  
Author(s):  
Stefania Piazza ◽  
E. J. Mirjam Blokker ◽  
Gabriele Freni ◽  
Valeria Puleo ◽  
Mariacrocetta Sambito
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Water Distribution ◽  
Water Quality Monitoring ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Optimization Approach ◽  
Monitoring Networks ◽  
Network Modelling ◽  
Nsga Ii

Abstract In recent years, there has been a need to seek adequate preventive measures to deal with contamination in water distribution networks that may be related to the accidental contamination and the deliberate injection of toxic agents. Therefore, it is very important to create a sensor system that detects contamination events in real time, maintains the reliability and efficiency of measurements, and limits the cost of the instrumentation. To this aim, two problems have to be faced: practical difficulties connected to the experimental verification of the optimal sensor configuration efficiency on real operating systems and challenges related to the reliability of the network modelling approaches, which usually neglect the dispersion and diffusion phenomena. The present study applies a numerical optimization approach using the NSGA-II genetic algorithm that was coupled with a new diffusive-dispersive hydraulic simulator. The results are compared with those of an experimental campaign on a laboratory network (Enna, Italy) equipped with a real-time water quality monitoring system and those of a full-scale real distribution network (Zandvoort, Netherlands). The results showed the importance of diffusive processes when flow velocity in the network is low. Neglecting diffusion can negatively influence the water quality sensor positioning, leading to inefficient monitoring networks.

scholarly journals Testing evolutionary algorithms for optimization of water distribution networks

2019 ◽  
Vol 46 (5) ◽  
pp. 391-402 ◽  
Author(s):  
Naser Moosavian ◽  
Barbara Lence
Keyword(s):  
Evolutionary Algorithms ◽  
Water Distribution ◽  
Optimization Algorithms ◽  
Distribution Networks ◽  
Global Optimum ◽  
Water Distribution Networks ◽  
Large Networks ◽  
Construction Design ◽  
Covariance Matrix Adaptation ◽  
Discrete Nature

Water distribution networks (WDNs) are one of the most important elements of urban infrastructure and require large investment for construction. Design of WDNs is classified as a large combinatorial discrete nonlinear optimization problem. The main concerns associated with the optimization of such networks are the nonlinearity of the discharge-head loss relationships for pipes and the discrete nature of pipe sizes. Due to these issues, this problem is widely considered to be a benchmark problem for testing and evaluating the performance of nonlinear and heuristic optimization algorithms. This paper compares different techniques, all based on evolutionary algorithms (EAs), which yield optimal solutions for least-cost design of WDNs. All of these algorithms search for the global optimum starting from populations of solutions, rather than from a single solution, as in Newton-based search methods. They use different operators to improve the performance of many solutions over repeated iterations. Ten EAs, four of them for the first time, are applied to the design of three networks and their performance in terms of the least cost, under different stopping criteria, are evaluated. Statistical information for 20 executions of the ten algorithms is summarized, and Friedman tests are conducted. Results show that, for the two-loop benchmark network, the particle swarm optimization gravitational search and biology and bioinformatics global optimization algorithms efficiently converge to the global optimum, but perform poorly for large networks. In contrast, given a sufficient number of function evaluations, the covariance matrix adaptation evolution strategy and soccer league competition algorithm consistently converge to the global optimum, for large networks.

scholarly journals Contribution of parallel NSGA-II in optimal design of water distribution networks

2011 ◽  
Vol 14 (2) ◽  
pp. 310-323 ◽  
Author(s):  
Sandro Artina ◽  
Cristiana Bragalli ◽  
Giovanni Erbacci ◽  
Angela Marchi ◽  
Marzia Rivi
Keyword(s):  
Optimal Design ◽  
Water Distribution ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Distribution Networks ◽  
Global Model ◽  
Water Distribution Networks ◽  
Medium Size ◽  
Nsga Ii ◽  
Time Required

Optimization of water distribution networks is a NP-hard problem that researchers have tried to deal with using different formulations and algorithmic approaches. Among these, multi-objective heuristic algorithms are interesting because of their capacity for dealing with separate objectives that allow us to choose a posteriori the best compromise, but one of their main drawbacks is the long time required to obtain good solutions. Parallel processing is the most promising way to reduce the computing time and can make the convergence to adequate solutions faster. This paper intends to investigate the possibility of improving the efficacy and efficiency of an NSGA-II algorithm by parallelization of the optimization process at the same time. Results of different parallel implementations of NSGA-II applied to optimal design of small- and medium-size water distribution networks are presented. Good speed-up can be reached with a global model, hence improving the algorithm efficiency. Unlike the global model, the island model (or the hierarchical parallelization) can also improve its efficacy because it introduces fundamental changes in the algorithm exploration method. Possibilities offered by parallel island models have been investigated showing that some parameter configurations can find better solutions compared with the serial version of the algorithm.

scholarly journals jHawanet: An Open-Source Project for the Implementation and Assessment of Multi-Objective Evolutionary Algorithms on Water Distribution Networks

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2018
Author(s):  
Jimmy H. Gutiérrez-Bahamondes ◽  
Yamisleydi Salgueiro ◽  
Sergio A. Silva-Rubio ◽  
Marco A. Alsina ◽  
Daniel Mora-Meliá ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Open Source ◽  
Network Model ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Multi Objective Optimization ◽  
Open Source Project ◽  
Multi Objective ◽  
Hydraulic Network

Efficient design and management of water distribution networks is critical for conservation of water resources and minimization of both energy requirements and maintenance costs. Several computational routines have been proposed for the optimization of operational parameters that govern such networks. In particular, multi-objective evolutionary algorithms have proven to be useful both properly describing a network and optimizing its performance. Despite these computational advances, practical implementation of multi-objective optimization algorithms for water networks is an abstruse subject for researchers and engineers, particularly since efficient coupling between multi-objective algorithms and the hydraulic network model is required. Further, even if the coupling is successfully implemented, selecting the proper set of multi-objective algorithms for a given network, and addressing the quality of the obtained results (i.e., the approximate Pareto frontier) introduces additional complexities that further hinder the practical application of these algorithms. Here, we present an open-source project that couples the EPANET hydraulic network model with the jMetal framework for multi-objective optimization, allowing flexible implementation and comparison of different metaheuristic optimization algorithms through statistical quality assessment. Advantages of this project are discussed by comparing the performance of different multi-objective algorithms (i.e., NSGA-II, SPEA2, SMPSO) on case study water pump networks available in the literature.

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