An enhanced binary dragonfly algorithm based on a V-shaped transfer function for optimization of pump scheduling program in water supply systems (case study of Iran)

•A binary dragonfly algorithm (BDA) with a new transfer function is proposed.•The proposed BDA is applied to minimize the energy consumption of pumping stations.•Results show the high performance of BDA comparing to the excited models. With the continual growth of population and shortage of energy r...

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Bibliographic Details
Published in:Engineering failure analysis Vol. 123; p. 105323
Main Authors: Jafari-Asl, Jafar, Azizyan, Gholamreza, Monfared, Seyed Arman Hashemi, Rashki, Mohsen, Andrade-Campos, Antonio G.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2021
Subjects:
Binary dragonfly algorithm
Energy cost
Pump scheduling program
Transfer function
Water supply system
Binary dragonfly algorithm
Water supply system
Energy cost
Transfer function
Pump scheduling program
ISSN:1350-6307, 1873-1961
Online Access:Get full text
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Summary:•A binary dragonfly algorithm (BDA) with a new transfer function is proposed.•The proposed BDA is applied to minimize the energy consumption of pumping stations.•Results show the high performance of BDA comparing to the excited models. With the continual growth of population and shortage of energy resources, the optimal consumption of these resources is of particular importance. One of these energy sources is electricity, with a significant amount being used in pumping stations for water distribution systems (WDS). Determining the proper pumping schedule can make significant savings in energy consumption and particularly in costs. This study aims to present an improved population-based nature-inspired optimization algorithmfor pumping scheduling program in WDS. To address this issue, the binary dragonfly algorithm based on a new transfer-function coupled with the EPANET hydraulic simulation model is developed to reduce the energy consumption of pumping stations. The proposed model was firstly implemented and evaluated on a benchmark test example, then on a real water pumping station. Comparison of the proposed method and the genetic algorithm (GA), evolutionary algorithm (EA), ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO), and firefly (FF) was conducted on the benchmark test example, while the obtained results indicate that the proposed framework is more computationally efficient and reliable. The results of the real case study show that while considering all different constraints of the problem, the proposed model can decrease the cost of energy up to 27% in comparison with the current state of operation.
ISSN:1350-6307
1873-1961
DOI:10.1016/j.engfailanal.2021.105323