Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem
Extensive work has been reported for the optimization of water distribution networks (WDNs) using different optimization techniques. Out of these techniques, evolutionary algorithms (EAs) were found to be more efficient as compared with conventional techniques like linear programming and dynamic pro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2021-09-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/12/6/2299 |
| _version_ | 1819012705869103104 |
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| author | Nikita Palod Vishnu Prasad Ruchi Khare |
| author_facet | Nikita Palod Vishnu Prasad Ruchi Khare |
| author_sort | Nikita Palod |
| collection | DOAJ |
| description | Extensive work has been reported for the optimization of water distribution networks (WDNs) using different optimization techniques. Out of these techniques, evolutionary algorithms (EAs) were found to be more efficient as compared with conventional techniques like linear programming and dynamic programming. Most of the EAs are complex meta-heuristics techniques and need tuning of algorithm-specific parameters. Rao algorithms (Rao-I and Rao-II) do not need any algorithm-specific parameters and hence eliminate the process of sensitivity analysis. In the present work, Rao algorithms are applied for the optimal pipe sizing of WDNs. The optimization results in terms of optimal pipe diameters and the number of evaluations for five different benchmark networks are compared with other EAs. For the two-loop, Hanoi, Go-Yang, and Kadu network, computational efficiency in terms of minimum function evaluations for Rao-I and Rao-II is found to be greater than 78.5 and 83.58%, respectively, when compared with the largest number of minimum function evaluations for other evolutionary techniques. It is seen that Rao algorithms are simple to apply and efficient and do not need any parameter tuning which reduces a large number of computational efforts. HIGHLIGHTS
Rao algorithms are applied for the optimization of pipe networks for the very first time.;
These are parameterless techniques and hence do not require sensitivity analysis.;
Reduces the computational efforts to a large extent.;
Applied and tested on five benchmark networks.;
Compared with other evolutionary techniques based on minimum function evaluations and found to be highly efficient.; |
| first_indexed | 2024-12-21T01:48:18Z |
| format | Article |
| id | doaj.art-3e1e64231a6c4a98a8357d8010b4a43c |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-12-21T01:48:18Z |
| publishDate | 2021-09-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-3e1e64231a6c4a98a8357d8010b4a43c2022-12-21T19:19:58ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542021-09-011262299231310.2166/wcc.2021.288288Redefining the application of an evolutionary algorithm for the optimal pipe sizing problemNikita Palod0Vishnu Prasad1Ruchi Khare2 Department of Civil Engineering, MANIT, Bhopal, Madhya Pradesh 462003, India Department of Civil Engineering, MANIT, Bhopal, Madhya Pradesh 462003, India Department of Civil Engineering, MANIT, Bhopal, Madhya Pradesh 462003, India Extensive work has been reported for the optimization of water distribution networks (WDNs) using different optimization techniques. Out of these techniques, evolutionary algorithms (EAs) were found to be more efficient as compared with conventional techniques like linear programming and dynamic programming. Most of the EAs are complex meta-heuristics techniques and need tuning of algorithm-specific parameters. Rao algorithms (Rao-I and Rao-II) do not need any algorithm-specific parameters and hence eliminate the process of sensitivity analysis. In the present work, Rao algorithms are applied for the optimal pipe sizing of WDNs. The optimization results in terms of optimal pipe diameters and the number of evaluations for five different benchmark networks are compared with other EAs. For the two-loop, Hanoi, Go-Yang, and Kadu network, computational efficiency in terms of minimum function evaluations for Rao-I and Rao-II is found to be greater than 78.5 and 83.58%, respectively, when compared with the largest number of minimum function evaluations for other evolutionary techniques. It is seen that Rao algorithms are simple to apply and efficient and do not need any parameter tuning which reduces a large number of computational efforts. HIGHLIGHTS Rao algorithms are applied for the optimization of pipe networks for the very first time.; These are parameterless techniques and hence do not require sensitivity analysis.; Reduces the computational efforts to a large extent.; Applied and tested on five benchmark networks.; Compared with other evolutionary techniques based on minimum function evaluations and found to be highly efficient.;http://jwcc.iwaponline.com/content/12/6/2299benchmark networksevolutionary algorithmoptimizationrao techniquewater supply |
| spellingShingle | Nikita Palod Vishnu Prasad Ruchi Khare Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem Journal of Water and Climate Change benchmark networks evolutionary algorithm optimization rao technique water supply |
| title | Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem |
| title_full | Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem |
| title_fullStr | Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem |
| title_full_unstemmed | Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem |
| title_short | Redefining the application of an evolutionary algorithm for the optimal pipe sizing problem |
| title_sort | redefining the application of an evolutionary algorithm for the optimal pipe sizing problem |
| topic | benchmark networks evolutionary algorithm optimization rao technique water supply |
| url | http://jwcc.iwaponline.com/content/12/6/2299 |
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