Developing a new algorithm (G-JPSO) for optimal control of pumps in water distribution networks

Document Type : Article

Authors

1 Department of Civil Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran

2 Department of Civil and Environmental Engineering, Environmental Research and Sustainable Development Center, Shiraz University, Shiraz, Iran.

3 Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran.

Abstract

Meta-heuristic methods have been widely used for solving complex problems recently. Among these methods, JPSO is regarded as a promising algorithm. However, in order to achieve more robust performance, the probability to solve the graph-based problems is modified by changing the jumping nature of this algorithm and a new algorithm called G-JPSO is presented which is evaluated by solving Fletcher-Powell function and optimal control of pumps in water distribution network problems. In addition to reduction of electricity cost and the problem limitations such as minimum required pressure in each node, minimum and maximum height of tanks, should also be considered. Moreover, another limitation was performed on the objective function which includes the maximum times of turning the pumps on and off. In order to determine the pumps optimal operation, an optimization-simulation model based on the optimization algorithms G-JPSO and JPSO is developed. This proposed model is used for determination of optimal operation program of Van Zyl distribution network. The comparison carried out between the results of our proposed algorithm and those of the similar algorithms including ant colony, genetic and JPSO shows the high ability of the presented algorithm in finding solutions near the optimal solutions with reasonable computation costs.

Keywords

Main Subjects

References

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Scientia Iranica
Volume 27, Issue 1
Transactions on Civil Engineering (A)
January and February 2020
Pages 68-79

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History

  • Receive Date: 28 May 2017
  • Revise Date: 12 December 2017
  • Accept Date: 02 July 2018

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