Design of water distribution networks using accelerated momentum particle swarm optimisation technique

Aghdam, Kazem Mohammadi; Mirzaee, Iraj; Pourmahmood, Nader; Aghababa, Mohammad Pourmahmood

doi:10.1080/0952813x.2013.863227

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…In the mid 1990s, after the first popular applications of a GA [20,151], there was a swing towards stochastic methods and they dominate the field since (see Figure 4). A great range of those methods has been applied to optimise design of WDSs to date, inclusive of (but not limited to) a GA [42,45,50,85,86,[152][153][154], fmGA [88], non-crossover dither creeping mutation-based GA (CMBGA) [149], adaptive locally constrained GA (ALCO-GA) [155], SA [60], shuffled frog leaping algorithm (SFLA) [103], ACO [104,156], shuffled complex evolution (SCE) [157], harmony search (HS) [105,158,159], particle swarm HS (PSHS) [160], parameter setting free HS (PSF HS) [161], combined cuckoo-HS algorithm (CSHS) [162], particle swarm optimisation (PSO) [106,153,154], improved PSO (IPSO) [163], accelerated momentum PSO (AMPSO) [164], integer discrete PSO (IDPSO) [165], newly developed swarm-based optimisation (DSO) algorithm [150], scatter search (SS) [166], CE [61,62], immune algorithm (IA) [167], heuristic-based algorithm (HBA) [168], memetic algorithm (MA) [107], genetic heritage evolution by stochastic transmission (GHEST) [169], honey bee mating optimisation (HBMO) …”

Section: Solution Methodologymentioning

confidence: 99%

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Mala-Jetmarova

Sultanova

Savić

2018

Water

130

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Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details.

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Section: Solution Methodologymentioning

confidence: 99%

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Mala-Jetmarova

Sultanova

Savić

2018

Water

130

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“…In addition, machine learning has played an important role in water-related engineering applications, such as reservoir operation [20,21], water management [22,23], turbine operation [24,25], water distribution network [26,27], sediment settling velocity prediction [28], intelligence hydrological model [29,30], rheological prediction model [31,32] etc. Therefore, it is believed that such a method is also applicable for the study of motion characteristics of the single bubbles.…”

Section: Motivationmentioning

confidence: 99%

NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water

et al. 2019

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Under the action of gravity, buoyancy, and surface tension, bubbles generated by wave breaking will rupture and polymerize, causing the occurrence of high-speed jets and strong turbulence in nearby water bodies, which in turn affects sea–air exchange, sediment transport, and pollutant movement. These interactions are closely related to the shape and velocity changes in single bubbles. Therefore, understanding the motion characteristics of single bubbles is essential. In this research, a large number of experiments were carried out to serve this purpose. The experimental data were used to develop three machine learning models for the bubble final velocity, bubble drag coefficient, and bubble shape, respectively. The performance of the feed forward back propagation neural network (FBNN) models for the final velocity and drag coefficient were evaluated. The coefficient of determination (R2) and root mean squared error (RMSE) value of final velocity prediction model was recorded at 0.83 and 0.0518, respectively. Meanwhile, for the drag coefficient prediction model, the values are 0.92 for R2 and 0.1534 for RMSE. The models can provide a more accurate output if compared to that from the empirical formulas. K-nearest neighbours (KNN), logistic regression, and random forest were applied as the algorithm while developing the bubble shape classification model. The best performance is achieved by the logistic regression.

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Adaptive Mutated Momentum Shuffled Frog Leaping Algorithm for Design of Water Distribution Networks

et al. 2014

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Design of water distribution networks using accelerated momentum particle swarm optimisation technique

Cited by 10 publications

References 24 publications

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water

Adaptive Mutated Momentum Shuffled Frog Leaping Algorithm for Design of Water Distribution Networks

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