Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization

Mora-Meliá, Daniel; Gutiérrez-Bahamondes, Jimmy H.; Iglesias-Rey, Pedro L.; Martínez-Solano, F. Javier

doi:10.3390/w8120583

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…The approach optimizes the parameters by means of genetic algorithms with hydraulic modeling interface, and to simulate the location of leaks in EPANET, ExpaGIS, and Optim-Detec software Mora-Melià [40] Different population-based algorithms…”

Section: Water Distribution Networkmentioning

confidence: 99%

Surveying the Optimization Problems of Water Distribution Networks

Ruiz-Vanoye¹,

Barrera-Cámara²,

Díaz-Parra³

et al. 2018

Pol. J. Environ. Stud.

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Pipelines [1] transport such resources as water, petroleum products, telecommunications, chemicals, natural gas, sewage, beer, biofuels (ethanol and biobutanol), and hydrogen. The Locations are residential and commercial areas, treatment plants, processing facilities, gas stations, pumping stations, terminals, tanks, storage facilities, partial delivery stations, inlet stations, injection stations, block valve stations, regulator stations, final delivery stations, floors (levels, decks) of a building, vessels, or other structures [1]. Water transportation problems (WTP) is a generic name given to a whole class of problems in which water transportation is necessary by pipelines or other means. Water distribution networks carry resources (drinking water, purified water, deionized water, distilled water, hard water, heavy water, soft water, tritiated water) to such locations as water storage facilities by pipelines and ducts. A water distribution network is a hydraulic infrastructure that is part of the water supply system composed of a set of pipes, hydraulic devices (pumps, pressure-reducing valves, etc.), and reservoirs [2]. The problems that arise in the distribution of water are usually the following: distribution of cities lack adequate modernization and maintenance, which cause loss of water resources, and poor distribution of water as a consequence causes the water to run through more miles to reach its destination. Traditionally networks of water

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Section: Water Distribution Networkmentioning

confidence: 99%

Surveying the Optimization Problems of Water Distribution Networks

Ruiz-Vanoye¹,

Barrera-Cámara²,

Díaz-Parra³

et al. 2018

Pol. J. Environ. Stud.

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“…One of these factors is the population size. Mora-Melià et al [52] studied population size in a pipesizing problem of water network optimization in different-sized networks with the Pseudo-Genetic Algorithm, the Harmony Search Optimization Algorithm, the modified Particle Swarm Algorithm, and the modified Shuffled Frog Leaping Algorithm. They showed that using a small population size is more efficient out of all the sizes of networks [52].…”

Section: Introductionmentioning

confidence: 99%

“…Mora-Melià et al [52] studied population size in a pipesizing problem of water network optimization in different-sized networks with the Pseudo-Genetic Algorithm, the Harmony Search Optimization Algorithm, the modified Particle Swarm Algorithm, and the modified Shuffled Frog Leaping Algorithm. They showed that using a small population size is more efficient out of all the sizes of networks [52]. Gao and Chen [53] studied the prediction of service life for tunnel structures in carbonation environments and the effects of population size and sample size with genetic programming.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effect of Sewer Network Size on Optimization Algorithms’ Performance in Sewer System Optimization

Turan,

Cetin

2024

Water

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Sewer systems are a component of city infrastructure that requires large investment in construction and operation. Metaheuristic optimization methods have been used to solve sewer optimization problems. The aim of this study is to investigate the effects of network size on metaheuristic optimization algorithms. Cuckoo Search (CS) and four versions of Grey Wolf Optimization (GWO) were utilized for the hydraulic optimization of sewer networks. The purpose of using different algorithms is to investigate whether the results obtained differ depending on the algorithm. In addition, to eliminate the parameter effect, the relevant algorithms were run with different parameters, such as population size. These algorithms were performed on three different-sized networks, namely small-sized, medium-sized, and large-sized networks. Friedman and Wilcoxon tests were utilized to statistically analyze the results. The results were also evaluated in terms of the optimality gap criterion. According to the results based on the optimality gap, the performance of each algorithm decreases as the network size increases.

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“…Thereinto, the population size is one of key control parameters in BIOs (Alanis et al, 2018). A mass of studies have shown that the population size can greatly influence the solving performance of BIOs in real engineering applications (Jansen et al, 2005;Diaz-Gomez and Hougen, 2007;Brest and Maucec, 2008;Mora-Melià et al, 2016;Castelli et al, 2017). Therefore, a prerequisite evaluating objectively estimation performance of algorithms in source inversion is to clarify the influence law of the population size in algorithm on source inversion.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Impacts of Typical Bio-Inspired Optimization Algorithms on Source Inversion Performance

Mao

Lang

et al. 2022

Front. Environ. Sci.

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Accurate identification of source information (i.e., source strength and location) is crucial for the air pollution control or effective accidental response. Optimization inversion based on bio-inspired algorithms (BIOs) is an effective method for estimating source information. However, the impacts of different BIOs and the shared parameter of population size in BIOs on source inversion performance have not been revealed. Here the source inversion performance (i.e., accuracy and robustness) of six typical BIOs [i.e., bacterial foraging optimization algorithm (BFO), chicken swarm optimization algorithm (CSO), differential evolution algorithm (DE), genetic algorithm (GA), particle swarm optimization (PSO), and seeker optimization algorithm (SOA)], and their population sizes are evaluated based on the Prairie Grass dataset which covering different atmospheric conditions (i.e., Pasquill stability classes A, B, C, D, E, and F). Results indicated the population size has substantial influence on source inversion. The accuracy of all BIOs in source strength fluctuated greatly when the population size was small, whereas, tended to be stable as the population size increased. Overall, the BFO had the best accuracy with lowest deviations (74.5% for source strength and 29.7 m for location parameter x0), whereas SOA had the best robustness for all source parameters. Atmospheric conditions indicated an obvious influence on the inversion performance of the BIOs. The BFO and CSO performed the best with the lowest deviations [137.5 and 26.7% for unstable conditions (A, B, and C) and stable condition (E), respectively], all algorithms are comparable (67.4 ± 2.1%) in neutral condition (D), and BFO and CSO had the comparable performances (23.2 and 24.3%) and performed better under extremely stable condition (F). This study enhances the understanding of the factors inﬂuencing source inversion and provides a reference for the selection of appropriate bio-inspired algorithms and the reasonable setting of population size parameter for source inversion in practical environmental management.

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Efficiency Criteria as a Solution to the Uncertainty in the Choice of Population Size in Population-Based Algorithms Applied to Water Network Optimization

Cited by 4 publications

References 38 publications

Surveying the Optimization Problems of Water Distribution Networks

Surveying the Optimization Problems of Water Distribution Networks

Analyzing the Effect of Sewer Network Size on Optimization Algorithms’ Performance in Sewer System Optimization

Comparative Study of Impacts of Typical Bio-Inspired Optimization Algorithms on Source Inversion Performance

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