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Relevant bibliographies by topics / Water distribution systems; multiobjective optimization / Journal articles

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

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Gupta, Aditya Dinesh, Neeraj Bokde, Dushyat Marathe, and Kishore Kulat. "Optimization techniques for leakage management in urban water distribution networks." Water Supply 17, no. 6 (April 17, 2017): 1638–52. http://dx.doi.org/10.2166/ws.2017.064.

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Abstract Reduction of leakages in water distribution systems (WDSs) is one of the major concerns for water industries. This paper presents a leakage reduction technique using pressure management by optimizing the water level in storage tanks, along with optimized control and localization of pressure-reducing valves (PRVs) in WDSs. A new mathematical tank and pump simulation algorithm is presented for controlling pressure in WDSs, by optimizing the water storage level in the tank depending upon the demand variations. The tank is used as a decision variable for the leakage reduction model. A modified reference pressure algorithm is introduced for improving PRV localization. A multiobjective genetic algorithm (NSGA-II) is used to find the optimized operational control setting of the PRV for leakage minimization. The proposed algorithm leads to a leakage reduction of 26.51% in Anytown WDS and 20.81% in a modified benchmark WDS. This technique leads to an appreciable reduction in leakage rate, with fewer PRVs required, taking into account constraints such as maintaining a lower hydraulic failure index (<0.01), emergency storage, etc. It can be concluded that the proposed novel leakage reduction technique provides a more cost effective and efficient solution for leakage control.

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12

Wu, Wenyan, Holger R. Maier, and Angus R. Simpson. "Multiobjective optimization of water distribution systems accounting for economic cost, hydraulic reliability, and greenhouse gas emissions." Water Resources Research 49, no. 3 (March 2013): 1211–25. http://dx.doi.org/10.1002/wrcr.20120.

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13

Izquierdo, Joaquín, Idel Montalvo, Rafael Pérez-García, and Agustín Matías. "On the Complexities of the Design of Water Distribution Networks." Mathematical Problems in Engineering 2012 (2012): 1–25. http://dx.doi.org/10.1155/2012/947961.

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Water supply is one of the most recognizable and important public services contributing to quality of life. Water distribution networks (WDNs) are extremely complex assets. A number of complex tasks, such as design, planning, operation, maintenance, and management, are inherently associated with such networks. In this paper, we focus on the design of a WDN, which is a wide and open problem in hydraulic engineering. This problem is a large-scale combinatorial, nonlinear, nonconvex, multiobjective optimization problem, involving various types of decision variables and many complex implicit constraints. To handle this problem, we provide a synergetic association between swarm intelligence and multiagent systems where human interaction is also enabled. This results in a powerful collaborative system for finding solutions to such a complex hydraulic engineering problem. All the ingredients have been integrated into a software tool that has also been shown to efficiently solve problems from other engineering fields.

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14

Wu, Wenyan, Holger R. Maier, and Angus R. Simpson. "Single-Objective versus Multiobjective Optimization of Water Distribution Systems Accounting for Greenhouse Gas Emissions by Carbon Pricing." Journal of Water Resources Planning and Management 136, no. 5 (September 2010): 555–65. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000072.

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15

Zeidan, Mohamad, Pu Li, and Avi Ostfeld. "DMA Segmentation and Multiobjective Optimization for Trading Off Water Age, Excess Pressure, and Pump Operational Cost in Water Distribution Systems." Journal of Water Resources Planning and Management 147, no. 4 (April 2021): 04021006. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001344.

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16

Bianchotti, Jezabel D., Melina Denardi, Mario Castro-Gama, and Gabriel D. Puccini. "Sectorization for Water Distribution Systems with Multiple Sources: A Performance Indices Comparison." Water 13, no. 2 (January 8, 2021): 131. http://dx.doi.org/10.3390/w13020131.

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Sectorization is an effective technique for reducing the complexities of analyzing and managing of water systems. The resulting sectors, called district metering areas (DMAs), are expected to meet some requirements and performance criteria such as minimum number of intervention, pressure uniformity, similarity of demands, water quality and number of districts. An efficient methodology to achieve all these requirements together and the proper choice of a criteria governing the sectorization is one of the open questions about optimal DMAs design. This question is addressed in this research by highlighting the advantages of three different criteria when applied to real-word water distribution networks (WDNs). To this, here it is presented a two-stage approach for optimal design of DMAs. The first stage, the clustering of the system, is based on a Louvain-type greedy algorithm for the generalized modularity maximization. The second stage, the physical dividing of the system, is stated as a two-objective optimization problem that utilises the SMOSA version of simulated annealing for multiobjective problems. One objective is the number of isolation valves whereas for the second objective three different performance indices (PIs) are analyzed and compared: (a) standard deviation, (b) Gini coefficient and (c) loss of resilience. The methodology is applied to two real case studies where the first two PIs are optimized to address similar demands among DMAs. The results demonstrate that the proposed method is effective for sectorization into independent DMAs with similar demands. Surprisingly, it found that for the real studied systems, loss of resilience achieves better performance for each district in terms of pressure uniformity and demand similarity than the other two specific performance criteria.

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17

Bianchotti, Jezabel D., Melina Denardi, Mario Castro-Gama, and Gabriel D. Puccini. "Sectorization for Water Distribution Systems with Multiple Sources: A Performance Indices Comparison." Water 13, no. 2 (January 8, 2021): 131. http://dx.doi.org/10.3390/w13020131.

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Sectorization is an effective technique for reducing the complexities of analyzing and managing of water systems. The resulting sectors, called district metering areas (DMAs), are expected to meet some requirements and performance criteria such as minimum number of intervention, pressure uniformity, similarity of demands, water quality and number of districts. An efficient methodology to achieve all these requirements together and the proper choice of a criteria governing the sectorization is one of the open questions about optimal DMAs design. This question is addressed in this research by highlighting the advantages of three different criteria when applied to real-word water distribution networks (WDNs). To this, here it is presented a two-stage approach for optimal design of DMAs. The first stage, the clustering of the system, is based on a Louvain-type greedy algorithm for the generalized modularity maximization. The second stage, the physical dividing of the system, is stated as a two-objective optimization problem that utilises the SMOSA version of simulated annealing for multiobjective problems. One objective is the number of isolation valves whereas for the second objective three different performance indices (PIs) are analyzed and compared: (a) standard deviation, (b) Gini coefficient and (c) loss of resilience. The methodology is applied to two real case studies where the first two PIs are optimized to address similar demands among DMAs. The results demonstrate that the proposed method is effective for sectorization into independent DMAs with similar demands. Surprisingly, it found that for the real studied systems, loss of resilience achieves better performance for each district in terms of pressure uniformity and demand similarity than the other two specific performance criteria.

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18

Gupta, A., N. Bokde, D. Marathe, and K. Kulat. "Leakage Reduction in Water Distribution Systems with Efficient Placement and Control of Pressure Reducing Valves Using Soft Computing Techniques." Engineering, Technology & Applied Science Research 7, no. 2 (April 24, 2017): 1528–34. http://dx.doi.org/10.48084/etasr.1032.

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Reduction of leakages in a water distribution system (WDS) is one of the major concerns of water industries. Leakages depend on pressure, hence installing pressure reducing valves (PRVs) in the water network is a successful techniques for reducing leakages. Determining the number of valves, their locations, and optimal control setting are the challenges faced. This paper presents a new algorithm-based rule for determining the location of valves in a WDS having a variable demand pattern, which results in more favorable optimization of PRV localization than that caused by previous techniques. A multiobjective genetic algorithm (NSGA-II) was used to determine the optimized control value of PRVs and to minimize the leakage rate in the WDS. Minimum required pressure was maintained at all nodes to avoid pressure deficiency at any node. Proposed methodology is applied in a benchmark WDS and after using PRVs, the average leakage rate was reduced by 6.05 l/s (20.64%), which is more favorable than the rate obtained with the existing techniques used for leakage control in the WDS. Compared with earlier studies, a lower number of PRVs was required for optimization, thus the proposed algorithm tends to provide a more cost-effective solution. In conclusion, the proposed algorithm leads to more favorable optimized localization and control of PRV with improved leakage reduction rate.

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19

Vamvakeridou-Lyroudia, L. S., G. A. Walters, and D. A. Savic. "Fuzzy Multiobjective Optimization of Water Distribution Networks." Journal of Water Resources Planning and Management 131, no. 6 (November 2005): 467–76. http://dx.doi.org/10.1061/(asce)0733-9496(2005)131:6(467).

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20

Wang, He, Qiuyu Cui, and Hualong Du. "Modeling and Optimization of Water Distribution in Mineral Processing considering Water Cost and Recycled Water." Computational Intelligence and Neuroscience 2022 (February 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/2314788.

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Reducing mineral processing water costs and freshwater consumption is a challenging task in the mineral processing water distribution (MPWD). The work presented in this paper focuses on two aspects of the MPWD optimization model and the MPWD optimization method. To achieve MPWD optimization effectively, a nonlinear constrained multiobjective model is built. The problem is formulated with two objectives of minimizing the mineral processing water costs and maximizing the amount of recycled water. In this paper, an optimization method named enhancing the multiobjective artificial bee colony (EMOABC) algorithm is proposed to solve this model. The EMOABC algorithm uses four strategies to obtain the Pareto-optimal solutions and to achieve the MPWD optimal solutions. With the three benchmark functions, the EMOABC algorithm outperforms the other two widely used algorithms in solving complex multiobjective optimization problems. The EMOABC algorithm is then applied to two cases. Results have shown that the proposed algorithm has the ability to solve the MPWD optimization model. The developed model and the proposed algorithm provide decision support for the actual MPWD problem.

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21

Raad, Darian, Alexander Sinske, and Jan van Vuuren. "Multiobjective Optimization for Water Distribution System Design Using a Hyperheuristic." Journal of Water Resources Planning and Management 136, no. 5 (September 2010): 592–96. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000061.

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22

Leung, Chris S. K., and Henry Y. K. Lau. "Multiobjective Simulation-Based Optimization Based on Artificial Immune Systems for a Distribution Center." Journal of Optimization 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/5852469.

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Competitive market factors, such as more stringent government regulations, larger number of competitors, and shorter product life cycle, in recent years have created more significant pressure on the management in all supply chain parties. To this end, the ability of analyzing and evaluating systems and related operations involving the deployment of complex multiobjective material handling systems is vital for distribution practitioners. In this respect, simulation modeling techniques together with optimization have emerged as a very useful tool to facilitate the effective analysis of these complex operations and systems. In this paper, we apply a multiobjective simulation-based optimization framework consisting of a hybrid immune-inspired algorithm named Suppression-controlled Multiobjective Immune Algorithm (SCMIA) and a simulation model for solving a real-life multiobjective optimization problem. The results show that the framework is able to solve large scale problems with a large number of parameters, operators, and equipment involved.

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23

Preis, Ami, and Avi Ostfeld. "Multiobjective Contaminant Sensor Network Design for Water Distribution Systems." Journal of Water Resources Planning and Management 134, no. 4 (July 2008): 366–77. http://dx.doi.org/10.1061/(asce)0733-9496(2008)134:4(366).

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24

Alonso, F. R., D. Q. Oliveira, and A. C. Zambroni de Souza. "Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration." IEEE Transactions on Power Systems 30, no. 2 (March 2015): 840–47. http://dx.doi.org/10.1109/tpwrs.2014.2330628.

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25

Pires, V. Fernão, Rui Lopes, and Dulce Costa. "Integration of storage systems in distribution networks through multiobjective optimization." Electrical Engineering 100, no. 3 (December 19, 2017): 1939–48. http://dx.doi.org/10.1007/s00202-017-0672-6.

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26

Odan, Frederico Keizo, Luisa Fernanda Ribeiro Reis, and Zoran Kapelan. "Real-Time Multiobjective Optimization of Operation of Water Supply Systems." Journal of Water Resources Planning and Management 141, no. 9 (September 2015): 04015011. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000515.

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27

Nafi, Amir, Caty Werey, and Patrick Llerena. "Water pipe renewal using a multiobjective optimization approach." Canadian Journal of Civil Engineering 35, no. 1 (January 2008): 87–94. http://dx.doi.org/10.1139/l07-075.

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Water utilities ensure the delivery of water to consumers through a pressured network composed of several hydraulic components: reservoirs, pipes, valves, and pumps. A right maintenance policy that takes into consideration both technical and economic factors must be applied to enhance the hydraulic performance and reliability of the water network. With the help of a multiobjective approach based on a Pareto ranking and a modified genetic algorithm, we propose a decision support model that ensures the scheduling of pipe renewal according to available financial resources. The model is based on forecasting pipe failures and evaluating future maintenance costs. Two indexes are used to measure the hydraulic deficiency in the water network after a failure occurrence. They measure the undelivered water quantity and the number of unsupplied nodes when a considered pipe is unavailable during the peak demand period. Both indices permit classification of pipes and help identify critical ones. Feasible solutions are assessed according to economic and technical objectives. The model proposes solutions that enhance the reliability of a water distribution network and reduce failure occurrences, thus giving better satisfaction to consumers.

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28

Quintiliani, Claudia, Oscar Marquez-Calvo, Leonardo Alfonso, Cristiana Di Cristo, Angelo Leopardi, Dimitri P. Solomatine, and Giovanni de Marinis. "Multiobjective Valve Management Optimization Formulations for Water Quality Enhancement in Water Distribution Networks." Journal of Water Resources Planning and Management 145, no. 12 (December 2019): 04019061. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0001133.

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29

González-Bravo, Ramón, Fabricio Nápoles-Rivera, José María Ponce-Ortega, and Mahmoud M. El-Halwagi. "Multiobjective Optimization of Dual-Purpose Power Plants and Water Distribution Networks." ACS Sustainable Chemistry & Engineering 4, no. 12 (October 5, 2016): 6852–66. http://dx.doi.org/10.1021/acssuschemeng.6b01817.

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30

Shirzad, Akbar, Massoud Tabesh, and Behzad Atayikia. "Multiobjective Optimization of Pressure Dependent Dynamic Design for Water Distribution Networks." Water Resources Management 31, no. 9 (April 28, 2017): 2561–78. http://dx.doi.org/10.1007/s11269-017-1602-0.

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31

Santos-Azevedo, Manoel Socorro, Ignacio Perez-Abril, Carlos de Leon-Benitez, Jandecy Cabral-Leite, and Ubiratan Holanda-Bezerra. "Multiobjective optimization of the reactive power compensation in electric distribution systems." DYNA 81, no. 187 (October 24, 2014): 175–83. http://dx.doi.org/10.15446/dyna.v81n187.40979.

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32

Meena, Nand Kishor, Sonam Parashar, Anil Swarnkar, Nikhil Gupta, and Khaleequr Rehman Niazi. "Improved Elephant Herding Optimization for Multiobjective DER Accommodation in Distribution Systems." IEEE Transactions on Industrial Informatics 14, no. 3 (March 2018): 1029–39. http://dx.doi.org/10.1109/tii.2017.2748220.

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33

Di Matteo, Michael, Graeme C. Dandy, and Holger R. Maier. "Multiobjective Optimization of Distributed Stormwater Harvesting Systems." Journal of Water Resources Planning and Management 143, no. 6 (June 2017): 04017010. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000756.

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34

Geem, Zong. "Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search." Water 7, no. 12 (July 8, 2015): 3613–25. http://dx.doi.org/10.3390/w7073613.

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35

Alfonso, Leonardo, Andreja Jonoski, and Dimitri Solomatine. "Multiobjective Optimization of Operational Responses for Contaminant Flushing in Water Distribution Networks." Journal of Water Resources Planning and Management 136, no. 1 (January 2010): 48–58. http://dx.doi.org/10.1061/(asce)0733-9496(2010)136:1(48).

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36

Niknam, Taher, and Mokhtar Sha Sadeghi. "An efficient evolutionary optimization algorithm for multiobjective distribution feeder reconfiguration." International Journal of Control, Automation and Systems 9, no. 1 (February 2011): 112–17. http://dx.doi.org/10.1007/s12555-011-0114-6.

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37

Barlow, Euan, and Tiku T. Tanyimboh. "Multiobjective Memetic Algorithm Applied to the Optimisation of Water Distribution Systems." Water Resources Management 28, no. 8 (April 23, 2014): 2229–42. http://dx.doi.org/10.1007/s11269-014-0608-0.

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38

Salazar Moreno, R., F. Szidarovszky, A. Rojano Aguilar, and I. López Cruz. "Multiobjective Linear Model Optimize Water Distribution in Mexican Valley." Journal of Optimization Theory and Applications 144, no. 3 (September 23, 2009): 557–73. http://dx.doi.org/10.1007/s10957-009-9608-2.

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39

Awe, O. M., S. T. A. Okolie, and O. S. I. Fayomi. "Optimization of Water Distribution Systems: A Review." Journal of Physics: Conference Series 1378 (December 2019): 022068. http://dx.doi.org/10.1088/1742-6596/1378/2/022068.

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40

Uluatam, Semra Siber. "LP optimization model for water distribution systems." International Journal of Environmental Studies 40, no. 1 (April 1992): 13–25. http://dx.doi.org/10.1080/00207239208710710.

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41

Bolognesi, A., C. Bragalli, C. Lenzi, and S. Artina. "Energy Efficiency Optimization in Water Distribution Systems." Procedia Engineering 70 (2014): 181–90. http://dx.doi.org/10.1016/j.proeng.2014.02.021.

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42

Kurek, Wojciech, and Avi Ostfeld. "Multiobjective Water Distribution Systems Control of Pumping Cost, Water Quality, and Storage-Reliability Constraints." Journal of Water Resources Planning and Management 140, no. 2 (February 2014): 184–93. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000309.

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43

Fu, Guangtao, Zoran Kapelan, and Patrick Reed. "Reducing the Complexity of Multiobjective Water Distribution System Optimization through Global Sensitivity Analysis." Journal of Water Resources Planning and Management 138, no. 3 (May 2012): 196–207. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000171.

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44

Zhang, Qingzhou, Zheng Yi Wu, Ming Zhao, Jingyao Qi, Yuan Huang, and Hongbin Zhao. "Automatic Partitioning of Water Distribution Networks Using Multiscale Community Detection and Multiobjective Optimization." Journal of Water Resources Planning and Management 143, no. 9 (September 2017): 04017057. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000819.

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45

Muslu, Mesut, and Max D. Anderson. "A multiobjective optimization method for electrical power distribution systems with load control alternatives." Electric Power Systems Research 16, no. 1 (January 1989): 1–9. http://dx.doi.org/10.1016/0378-7796(89)90031-x.

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46

Branco, H. M. G. C., M. Oleskovicz, D. V. Coury, and A. C. B. Delbem. "Multiobjective optimization for power quality monitoring allocation considering voltage sags in distribution systems." International Journal of Electrical Power & Energy Systems 97 (April 2018): 1–10. http://dx.doi.org/10.1016/j.ijepes.2017.10.011.

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47

Li, Bo, Jue Wang, and Nan Xia. "Optimal Scheduling of a Microgrid Using Multiobjective Biogeography-Based Optimization Model and Algorithm with Adaptive Migration." Mathematical Problems in Engineering 2020 (October 5, 2020): 1–15. http://dx.doi.org/10.1155/2020/7120352.

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Aiming at the important research topic of optimal scheduling in the microgrid field, the general model for multiobjective dynamic optimal scheduling of a microgrid is established with the objective of minimizing economic and environmental costs. On this basis, the model is organically integrated with constraint handling technology, multiobjective optimization, and biogeography-based optimization algorithm, and then a constrained multiobjective evolutionary model suitable for biogeography-based optimization is further established. The corresponding constraint handling mechanism, the determination method of habitat suitability index, and migration strategy are improved, and the convergence performance and the distribution uniformity of Pareto frontier for multiobjective evolutionary algorithm are effectively enhanced. Applied to the optimal scheduling of typical microgrid systems, the effectiveness of the proposed model and method is verified.

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48

Vasan, A., K. Srinivasa Raju, and B. Sriman Pankaj. "Fuzzy optimization-based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm." Water Supply 22, no. 3 (November 30, 2021): 3178–94. http://dx.doi.org/10.2166/ws.2021.410.

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Abstract Water Distribution Network(s) (WDN) design is gaining prominence in the urban planning context. Several factors that play a significant role in design are uncertainty in data, non-linear relation of head loss & discharge, combinatorial nature of the problem, and high computational requirements. In addition, many conflicting objectives are possible and required for effective WDN design, such as cost, resilience, and leakage. Most of the research work published has used multiobjective evolutionary optimization in solving such complex WDN. However, the challenge of such population-based evolutionary approaches is that they provide multiple trade-off Pareto optimal solutions to the decision-maker who will have to choose another set of techniques to arrive at a single optimal solution. The present study employs a fuzzy optimization approach that would provide a single optimal WDN design for Hanoi and Pamapur, India. Maximization of network resilience (NR) and minimization of network cost (NC) are employed in a multiobjective context. Later, minimization of network leakages (NL) is also incorporated, leading to three objective problems. Hyperbolic membership function (HMF), exponential membership function (EMF), and non-linear membership function (NMF) are employed in Self-Adaptive Cuckoo Search Algorithm-based fuzzy optimization. HMF is found suitable to determine the best possible WDN design for chosen case studies based on the highest degree of satisfaction.

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49

Tang, Xing Gang, Mehdi Rezoug, Rabbar Hamzaoui, David Bassir, Rani El Meouche, Jean François Khreim, and Zhi Qiang Feng. "Multiobjective Optimization on Urban Flooding Using RSM and GA." Advanced Materials Research 255-260 (May 2011): 1627–31. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.1627.

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Urban flooding due to climate changes, dam breaking and drainage systems being overwhelmed by rainfall causes a huge loss to mankind civilization every year all over the world. This work aims to minimize the flooding damage by optimally design the building location and interspace. The velocity, water pressure and the height of water are considered as the main factors to reduce the flooding damage. The multi-objective optimization is implemented by combing several techniques, including Design of Experiment (DOE), Response Surface Method (RSM) and the Genetic Algorithm (GA). The proposed methodology is validated with a case study on the topography of ESTP Cachan.

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Mortazavi-Naeini, Mohammad, George Kuczera, and Lijie Cui. "Application of multiobjective optimization to scheduling capacity expansion of urban water resource systems." Water Resources Research 50, no. 6 (June 2014): 4624–42. http://dx.doi.org/10.1002/2013wr014569.

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