Application of local and global particle swarm optimization algorithms to optimal design and operation of irrigation pumping systems

Afshar, M. H.; Rajabpour, R.

doi:10.1002/ird.412

Cited by 16 publications

(9 citation statements)

References 12 publications

(13 reference statements)

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“…It is proved that velocity would be crucial, because large values cause particles tending to their good solutions, while small values could result in insufficient exploration of the search (Afshar and Rajabpour 2009). This lack of a control mechanism for the velocity resulted in low efficiency for PSO is managed by Eq.…”

Section: Optimization Modelmentioning

confidence: 96%

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Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran

Afshar

Kazemi

Saadatpour

2011

Water Resour Manage

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An automatic calibration of water quality model is developed in this research. Automatic calibration as the process to determine the parameters appearing in the equations of a 2-dimensional, hydrodynamic, and water quality models (CE-QUAL-W2) is carried out with Particle Swarm technique as an optimization tool. In the calibration of the CE-QUAl-W2 model, evaporation as a significant parameter influences the thermal profile and water surface elevation in reservoir, simultaneously. Therefore to consider the simultaneous effects of the water temperature variations on water surface elevation in the reservoir, a multi objective technique is used to minimize the weighted sum of total deviations of temperature from field data at check points on monitoring days and those of water surface elevation on daily monitoring period. The proposed approach overcomes the high computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. The automatic calibration approach is applied in temperature and water budget calibration of Karkheh reservoir in Iran. Applying the proposed automatic calibration approach, shows the produced results by the CE-QUAL-W2 model with the calibrated coefficients agree closely with a set of field data.

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Section: Optimization Modelmentioning

confidence: 96%

“…. , pg D ), then the swarm is manipulated according to the following two equations (Afshar and Rajabpour 2009).…”

Section: Optimization Modelmentioning

confidence: 99%

Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran

Afshar

Kazemi

Saadatpour

2011

Water Resour Manage

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“…PSO algorithm developed by Kennedy and Eberhart [12], is a population-based heuristic search technique inspired by social behavior of bird flocking and fish schooling [5,17,24]. In this approach, global optimums can be searched as well as local optimal solutions [1,25,26]. Let t be a time instant the velocity and position of each particle are updated by Eqs.…”

Section: Pnn Optimized By Pso (Pso-pnn)mentioning

confidence: 99%

“…It should be noted that the searching domain of the σ parameter in this study has been restricted to the range σ ∈ [0. 1,5]. Here, it is assumed that the chosen values can cover a range of the parameter search space, which leads to a high prediction accuracy.…”

Section: Pnn Optimized By Pso (Pso-pnn)mentioning

confidence: 99%

Application of a Probabilistic Neural Network for Liquefaction Assessment

Xue¹,

Yang²

2017

NNW

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This paper describes the first attempt of hardware implementation of Multistream Compression (MSC) algorithm. The algorithm is transformed to series of Finite State Machines with Datapath using Register-Transfer methodology. Those state machines are then implemented in VHDL to selected FPGA platform. The algorithm utilizes a special tree data structure, called MSC tree. For storage purpose of the MSC tree a Left Tree Representation is introduced. Due to parallelism, the algorithm uses multiple port access to SDRAM memory.

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“…Since the constraints and objective functions are non-linear and the number of decision variables and constraints are very much, the problem of pumps optimal operation in water distribution network is a part of large non-linear problemsThe objective function is minimizes the operation objectives from pumping stations during a planning horizon, in which the system pumps out the water during this time [4].…”

Section: Introductionmentioning

confidence: 99%

Developing New Algorithm (G-JPSO) for Optimal Control of Pumps in Water Distribution Networks

2018

Self Cite

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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.

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Application of local and global particle swarm optimization algorithms to optimal design and operation of irrigation pumping systems

Cited by 16 publications

References 12 publications

Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran

Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran

Application of a Probabilistic Neural Network for Liquefaction Assessment

Developing New Algorithm (G-JPSO) for Optimal Control of Pumps in Water Distribution Networks

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