Relevant bibliographies by topics / Water distribution systems; multiobjective optimization

Academic literature on the topic 'Water distribution systems; multiobjective optimization'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Water distribution systems; multiobjective optimization"

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Lence, B. J., N. Moosavian, and H. Daliri. "Fuzzy Programming Approach for Multiobjective Optimization of Water Distribution Systems." Journal of Water Resources Planning and Management 143, no. 7 (July 2017): 04017020. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000769.

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Li, C., J. Z. Yu, T. Q. Zhang, X. W. Mao, and Y. J. Hu. "Multiobjective optimization of water quality and rechlorination cost in water distribution systems." Urban Water Journal 12, no. 8 (August 5, 2014): 646–52. http://dx.doi.org/10.1080/1573062x.2014.939093.

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Preis, Ami, and Avi Ostfeld. "Multiobjective contaminant response modeling for water distribution systems security." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 267–74. http://dx.doi.org/10.2166/hydro.2008.061.

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Following the events of 9/11/2001 in the US, the world public awareness to possible terrorist attacks on water supply systems has increased significantly. The security of drinking water distribution systems has become a foremost concern around the globe. Water distribution systems are spatially diverse and thus are inherently vulnerable to intentional contamination intrusions. In this study, a multiobjective optimization evolutionary model for enhancing the response against deliberate contamination intrusions into water distribution systems is developed and demonstrated. Two conflicting objectives are explored: (1) minimization of the contaminant mass consumed following detection, versus (2) minimization of the number of operational activities required to contain and flush the contaminant out of the system (i.e. number of valves closure and hydrants opening). Such a model is aimed at directing quantitative response actions in opposition to the conservative approach of entire shutdown of the system until flushing and cleaning is completed. The developed model employs the multiobjective Non-Dominated Sorted Genetic Algorithm–II (NSGA-II) scheme, and is demonstrated using two example applications.
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Zheng, Feifei, Angus R. Simpson, and Aaron C. Zecchin. "An efficient hybrid approach for multiobjective optimization of water distribution systems." Water Resources Research 50, no. 5 (May 2014): 3650–71. http://dx.doi.org/10.1002/2013wr014143.

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Tanyimboh, Tiku T., and Alemtsehay G. Seyoum. "Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible solutions." Journal of Environmental Management 183 (December 2016): 133–41. http://dx.doi.org/10.1016/j.jenvman.2016.08.048.

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Alvisi, Stefano, and Marco Franchini. "Multiobjective Optimization of Rehabilitation and Leakage Detection Scheduling in Water Distribution Systems." Journal of Water Resources Planning and Management 135, no. 6 (November 2009): 426–39. http://dx.doi.org/10.1061/(asce)0733-9496(2009)135:6(426).

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Ostfeld, Avi, Nurit Oliker, and Elad Salomons. "Multiobjective Optimization for Least Cost Design and Resiliency of Water Distribution Systems." Journal of Water Resources Planning and Management 140, no. 12 (December 2014): 04014037. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000407.

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Moosavian, N., and B. J. Lence. "Nondominated Sorting Differential Evolution Algorithms for Multiobjective Optimization of Water Distribution Systems." Journal of Water Resources Planning and Management 143, no. 4 (April 2017): 04016082. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000741.

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Wu, Wenyan, Angus R. Simpson, and Holger R. Maier. "Accounting for Greenhouse Gas Emissions in Multiobjective Genetic Algorithm Optimization of Water Distribution Systems." Journal of Water Resources Planning and Management 136, no. 2 (March 2010): 146–55. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000020.

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Herstein, L. M., Y. R. Filion, and K. R. Hall. "Evaluating the Environmental Impacts of Water Distribution Systems by Using EIO-LCA-Based Multiobjective Optimization." Journal of Water Resources Planning and Management 137, no. 2 (March 2011): 162–72. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000101.

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Dissertations / Theses on the topic "Water distribution systems; multiobjective optimization"

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Rogers, Scott W. "Multiobjective optimization of contaminant sensor locations in drinking water distribution systems using nodal importance concepts." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29607.

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Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Aral, Mustafa; Committee Member: Guan, Jiabao; Committee Member: Jang, Wonyong; Committee Member: Kim, Seong-Hee; Committee Member: Uzer, Turgay. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Formiga, Klebber Teodomiro Martins. "Otimização multiobjetivo de projetos de redes de distribuição de água." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18138/tde-29012016-125410/.

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O dimensionamento otimizado de sistemas de distribuição de águas tem originado centenas de trabalhos científicos nas últimas quatro décadas. Vários pesquisadores têm buscado encontrar uma metodologia capaz de dimensionar essas redes considerando diversos aspectos e incertezas características desse tipo de projeto. No entanto, os resultados da maioria das metodologias desenvolvidas não podem ser aplicados na prática. O objetivo deste trabalho é elaborar uma metodologia de dimensionamento de redes de distribuição de água considerando um enfoque multiobjetivo. A metodologia desenvolvida considera três aspectos referentes ao projeto desses sistemas: custo; confiabilidade e perdas por vazamentos. Para tanto, empregou-se um método de otimização multiobjetivo baseado em algoritmos genéticos para a geração do conjunto de soluções não-dominadas e um método multicriterial para escolha da alternativa final. Para representar os objetivos do problema, foram testadas nove funções: custo, vazamentos, entropia, resiliência, tolerância à falha, expansibilidade, efeito do envelhecimento e resilientropia, sendo que sete destas são específicas para a representação da confiabilidade. Para se avaliar as alternativas geradas foi desenvolvido um modelo de análise hidráulica que fosse capaz de trabalhar com vazamentos e com demandas dependente da pressão. Os métodos escolhidos foram o Híbrido de Nielsen e o Gradiente. Das funções testadas, a resilientropia, proposta originalmente neste trabalho, foi a que melhor se ajustou ao conceito formal de confiabilidade, representado pela função tolerância. Os resultados encontrados pela metodologia mostraram-se promissores, uma vez esta foi capaz de encontrar redes eficientes ao final das simulações.
The topic \"Optimized design of water distribution systems\" has generated hundreds of scientific publications in the last four decades. Several researchers have searched for a technology which would take into account a variety of aspects and uncertainties innate to the design of such networks. However, the results of most methodologies developed are not practical. The objective of this work is to develop a methodology for water distribution systems design that has a multi-objective focus. The methodology developed focuses in three aspects of the design of such systems: cost, reliability and losses by leaking. A multiobjective optimization method based on generic algorithms, generating a set of non-defined solutions, and a multi-criteria method for choosing the final alternative, was employed. Nine functions representing the objectives of the problem (method) were tested: cost, leakages, entropy, resilience, failure tolerance, expansibility, aging effect and resilienthropy, seven of which are specific to representing reliability. In order to evaluate the generated alternatives, a hydraulic analysis model, that could handle leakages and pressure dependent demands, was developed. The chosen methods were Nielsen\'s Hybrid, and the Gradient. Of all tested functions, resilientropy, originally proposed in this work, proved to be the one best adjusted to the formal concept of reliability, represented by the tolerance function. The results obtained by this methodology are promising, as they produced efficient distribution networks at the end of the simulations performed.
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Greene, James J. "Global optimization of water distribution systems." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-10062009-020212/.

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Hilber, Patrik. "Maintenance optimization for power distribution systems." Doctoral thesis, Stockholm : Electrical Engineering, Elektrotekniska system, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4686.

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Andrade-Rodriguez, Manuel Alejandro. "Computationally Intensive Design of Water Distribution Systems." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/301704.

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The burdensome capital cost of urban water distribution systems demands the use of efficient optimization methods capable of finding a relatively inexpensive design that guarantees a minimum functionality under all conditions of operation. The combinatorial and nonlinear nature of the optimization problem involved accepts no definitive method of solution. Adaptive search methods are well fitted for this type of problem (to which more formal methods cannot be applied), but their computational requirements demand the development and implementation of additional heuristics to find a satisfactory solution. This work seeks to employ adaptive search methods to enhance the search process used to find the optimal design of any water distribution system. A first study presented here introduces post-optimization heuristics that analyze the best design obtained by a genetic algorithm--arguably the most popular adaptive search method--and perform an ordered local search to maximize further cost savings. When used to analyze the best design found by a genetic algorithm, the proposed post-optimization heuristics method successfully achieved additional cost savings that the genetic algorithm failed to detect after an exhaustive search. The second study herein explores various ways to improve artificial neural networks employed as fast estimators of computationally intensive constraints. The study presents a new methodology for generating any large set of water supply networks to be used for the training of artificial neural networks. This dataset incorporates several distribution networks in the vicinity of the search space in which the genetic algorithm is expected to focus its search. The incorporation of these networks improved the accuracy of artificial neural networks trained with such a dataset. These neural networks consistently showed a lower margin of error than their counterparts trained with conventional training datasets populated by randomly generated distribution networks.
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Tsegaye, Seneshaw Amare. "Flexible Urban Water Distribution Systems." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4597.

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With increasing global change pressures such as urbanization and climate change, cities of the future will experience difficulties in efficiently managing scarcer and less reliable water resources. However, projections of future global change pressures are plagued with uncertainties. This increases the difficulty in developing urban water systems that are adaptable to future uncertainty. A major component of an urban water system is the distribution system, which constitutes approximately 80-85% of the total cost of the water supply system (Swamee and Sharma, 2008). Traditionally, water distribution systems (WDS) are designed using deterministic assumptions of main model input variables such as water availability and water demand. However, these deterministic assumptions are no longer valid due to the inherent uncertainties associated with them. Hence, a new design approach is required, one that recognizes these inherent uncertainties and develops more adaptable and flexible systems capable of using their active capacity to act or respond to future alterations in a timely, performance-efficient, and cost-effective manner. This study develops a framework for the design of flexible WDS that are adaptable to new, different, or changing requirements. The framework consists of two main parts. The first part consists of several components that are important in the pre and post--processing of the least-cost design methodology of a flexible WDS. These components include: the description of uncertainties affecting WDS design, identification of potential flexibility options for WDS, generation of flexibility through optimization, and a method for assessing of flexibility. For assessment a suite of performance metrics is developed that reflect the degree of flexibility of a distribution system. These metrics focus on the capability of the WDS to respond and react to future changes. The uncertainties description focuses on the spatial and temporal variation of future demand. The second part consists of two optimization models for the design of centralized and decentralized WDS respectively. The first model generates flexible, staged development plans for the incremental growth of a centralized WDS. The second model supports the development of clustered/decentralized WDS. It is argued that these clustered systems promote flexibility as they provide internal degrees of freedom, allowing many different combinations of distribution systems to be considered. For both models a unique genetic algorithm based flexibility optimization (GAFO) model was developed that maximizes the flexibility of a WDS at the least cost. The efficacy of the developed framework and tools are demonstrated through two case study applications on real networks in Uganda. The first application looks at the design of a centralized WDS in Mbale, a small town in Eastern Uganda. Results from this application indicate that the flexibility framework is able to generate a more flexible design of the centralized system that is 4% - 50% less expensive than a conventionally designed system when compared against several future scenarios. In addition, this application highlights that the flexible design has a lower regret under different scenarios when compared to the conventionally designed system (a difference of 11.2m3/US$). The second application analyzes the design of a decentralized network in the town of Aura, a small town in Northern Uganda. A comparison of a decentralized system to a centralized system is performed, and the results indicate that the decentralized system is 24% - 34% less expensive and that these cost savings are associated with the ability of the decentralized system to be staged in a way that traces the urban growth trajectory more closely. The decentralized clustered WDS also has a lower regret (a difference of 17.7m3/US$) associated with the potential future conditions in comparison with the conventionally centralized system and hence is more flexible.
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Morley, Mark S. "A framework for evolutionary optimization applications in water distribution systems." Thesis, University of Exeter, 2008. http://hdl.handle.net/10036/42400.

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The application of optimization to Water Distribution Systems encompasses the use of computer-based techniques to problems of many different areas of system design, maintenance and operational management. As well as laying out the configuration of new WDS networks, optimization is commonly needed to assist in the rehabilitation or reinforcement of existing network infrastructure in which alternative scenarios driven by investment constraints and hydraulic performance are used to demonstrate a cost-benefit relationship between different network intervention strategies. Moreover, the ongoing operation of a WDS is also subject to optimization, particularly with respect to the minimization of energy costs associated with pumping and storage and the calibration of hydraulic network models to match observed field data. Increasingly, Evolutionary Optimization techniques, of which Genetic Algorithms are the best-known examples, are applied to aid practitioners in these facets of design, management and operation of water distribution networks as part of Decision Support Systems (DSS). Evolutionary Optimization employs processes akin to those of natural selection and “survival of the fittest” to manipulate a population of individual solutions, which, over time, “evolve” towards optimal solutions. Such algorithms are characterized, however, by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of water networks incurs significant computational overheads, can limit the applicability and scalability of this technology in this domain. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to Water Distribution Systems. A number of new procedures are presented for improving the performance of such algorithms when applied to complex engineering problems. These techniques approach the problem of minimising the impact of the inherent computational complexity of these problems from a number of angles. A novel genetic representation is presented which combines the algorithmic simplicity of the classical binary string of the Genetic Algorithm with the performance advantages inherent in an integer-based representation. Further algorithmic improvements are demonstrated with an intelligent mutation operator that “learns” which genes have the greatest impact on the quality of a solution and concentrates the mutation operations on those genes. A technique for implementing caching of solutions – recalling the results for solutions that have already been calculated - is demonstrated to reduce runtimes for Genetic Algorithms where applied to problems with significant computation complexity in their evaluation functions. A novel reformulation of the Genetic Algorithm for implementing robust stochastic optimizations is presented which employs the caching technology developed to produce an multiple-objective optimization methodology that demonstrates dramatically improved quality of solutions for given runtime of the algorithm. These extensions to the Genetic Algorithm techniques are coupled with a supporting software library that represents a standardized modelling architecture for the representation of connected networks. This library gives rise to a system for distributing the computational load of hydraulic simulations across a network of computers. This methodology is established to provide a viable, scalable technique for accelerating evolutionary optimization applications.
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Van, Zyl Jakobus Ernst. "A methodology for improved operational optimization of water distribution systems." Thesis, University of Exeter, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366606.

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Guc, Gercek. "Optimization Of Water Distribution Networks Using Genetic Algorithm." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607192/index.pdf.

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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. Total cost achieved by RealPipe is 16.74 percent lower than the cost of the existing network
it should be noted that the solution provided by RealPipe is hydraulically improved.
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Rogalski, Richard Byron. "Mathematical and artificial neural network models for simulation and optimization of chlorine residuals in water distribution systems." access full-text online access from Digital Dissertation Consortium, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?NQ77034.

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Books on the topic "Water distribution systems; multiobjective optimization"

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ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai Shi: Shanghai da xue chu ban she, 2010.

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Haimes, Y. Y., W. A. Hall, and H. T. Freedman. Multiobjective Optimization in Water Resources Systems: The Surrogate Worth Trade-Off Method. Elsevier Science & Technology Books, 2011.

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Water Industry Systems: Modelling and Optimization Applications (Water Engineering and Management Series). Research Studies Pre, 1999.

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Multiobjective optimization of hybrid regenerative life support technologies, (topic D, technology assessment): NASA interim progress report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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United States. National Aeronautics and Space Administration., ed. Multiobjective optimization of hybrid regenerative life support technologies, (topic D, technology assessment): NASA interim progress report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Water Industry Systems: Modelling and Optimization Applicaitons (Water Engineering and Management Series, 3-4). Research Studies Pre, 1999.

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Jung, Bong Seog. Optimization and calibration of water distribution systems: Exploring evolutionary approaches while accounting for fluid transients. 2005.

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Book chapters on the topic "Water distribution systems; multiobjective optimization"

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Stachura, Marcin. "Partial Optimization of Water Distribution System Accounting for Multiobjective System Safety." In Progress in IS, 347–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44711-7_28.

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Sarbu, Ioan, and Emilian Stefan Valea. "Optimization of Path for Water Transmission and Distribution Systems." In Lecture Notes in Electrical Engineering, 323–39. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7684-5_23.

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Riza, Lala Septem, Azhari Fathurachman Azmi, Waslaluddin, Eka Fitrajaya Rahman, and Kuntjoro Adji Sidarto. "Particle Swarm Optimization for Calculating Pressure on Water Distribution Systems." In Lecture Notes in Computer Science, 381–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41000-5_38.

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Fanni, A., S. Liberatore, G. M. Sechi, M. Soro, and P. Zuddas. "Optimization of Water Distribution Systems by a Tabu Search Metaheuristic." In Operations Research/Computer Science Interfaces Series, 279–98. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4567-5_16.

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Mansouri, Ramin, Hasan Torabi, and Hosein Morshedzadeh. "Retracted Chapter: Optimization of Water Distribution Networks with Differential Evolution (DE)." In Advances in Intelligent Systems and Computing, 403–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47926-1_39.

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Kaveh, Ali, and Armin Dadras Eslamlou. "Colliding Bodies Optimization for Analysis and Design of Water Distribution Systems." In Metaheuristic Optimization Algorithms in Civil Engineering: New Applications, 237–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45473-9_11.

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Duckstein, L., and A. Tecle. "Multiobjective Analysis in Water Resources Part II — A New Typology of MCDM Techniques." In Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization, 333–43. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1697-8_21.

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Mansouri, Ramin, Hasan Torabi, and Hosein Morshedzadeh. "Retraction Note to: Optimization of Water Distribution Networks with Differential Evolution (DE)." In Advances in Intelligent Systems and Computing, E1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47926-1_44.

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Pombo, António Vieira, Vitor Fernão Pires, and João Murta Pina. "Application of NSGA-II Algorithm to Multiobjective Optimization of Switching Devices Placement in Electric Power Distribution Systems." In Technological Innovation for Collective Awareness Systems, 380–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54734-8_42.

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Sherali, Hanif D., Ernest P. Smith, and Seong-in Kim. "A Pipe Reliability and Cost Model for an Integrated Approach Toward Designing Water Distribution Systems." In Nonconvex Optimization and Its Applications, 333–54. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5331-8_11.

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Conference papers on the topic "Water distribution systems; multiobjective optimization"

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Babayan, A. V., D. A. Savic, and G. A. Walters. "Multiobjective Optimization for the Least-Cost Design of Water Distribution System Under Correlated Uncertain Parameters." In World Water and Environmental Resources Congress 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40792(173)36.

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Preis, Ami, and Avi Ostfeld. "Multiobjective Sensor Design for Water Distribution Systems Security." In Eighth Annual Water Distribution Systems Analysis Symposium (WDSA). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40941(247)107.

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Muleta, Misgana K., and Paul F. Boulos. "Multiobjective Optimization for Optimal Design of Urban Drainage Systems." In World Environmental and Water Resources Congress 2007. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40927(243)172.

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de Marinis, G., R. Gargano, Z. Kapelan, D. Savic, and C. Tricarico. "Multiobjective Optimal Rehabilitation of Hydraulic Networks based on the Cost of Reliability." In Eighth Annual Water Distribution Systems Analysis Symposium (WDSA). Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40941(247)176.

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Augugliaro, A., L. Dusonchet, S. Favuzza, M. G. Ippolito, and E. Riva Sanseverino. "Constrained Robust MultiObjective Optimization for Reactive Design in Distribution Systems." In 2006 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2006. http://dx.doi.org/10.1109/pmaps.2006.360313.

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Moskwa, Szczepan, Slawomir Koziel, Marek Siluszyk, and Zbigniew Galias. "Multiobjective Optimization for Switch Allocation in Radial Power Distribution Grids." In 2018 International Conference on Signals and Electronic Systems (ICSES). IEEE, 2018. http://dx.doi.org/10.1109/icses.2018.8507274.

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Bjork, Kaj-Mikael, and Lazaros G. Papageorgiou. "Global Optimization of Water Distribution Systems." In 2008 41st Annual Hawaii International Conference on System Sciences. IEEE, 2008. http://dx.doi.org/10.1109/hicss.2008.184.

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Fu, Guangtao, and Zoran Kapelan. "Embedding Neural Networks in Multiobjective Genetic Algorithms for Water Distribution System Design." In 12th Annual Conference on Water Distribution Systems Analysis (WDSA). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41203(425)81.

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Basile, N., M. Fuamba, and B. Barbeau. "Optimization of Water Tank Design and Location in Water Distribution Systems." In Water Distribution Systems Analysis 2008. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41024(340)32.

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Raad, D. N., A. N. Sinske, and J. H. van Vuuren. "Jumping Genes for Water Distribution System Optimization." In Water Distribution Systems Analysis 2008. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41024(340)38.

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Reports on the topic "Water distribution systems; multiobjective optimization"

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Energy optimization of water distribution systems. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/258148.

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