Parameter estimation of the soil water retention curve model with Jaya algorithm

Wang, Long; Huang, Lingmiao

doi:10.1016/j.compag.2018.06.024

Cited by 26 publications

(11 citation statements)

References 14 publications

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“…The modified model was tested on Korean weathered soils and accurately predicted the unsaturated behavior of the HC functions. Wang et al [18] proposed a method based on the Jaya algorithm to estimate the parameters of the van Genuchten model via four different soil samples, and the results showed the superiority of the proposed method. Matula et al [19] applied Woesten's continuous pedotransfer functions to 140 SWRCs measured in a selected location, and the results showed that the general equation of Woesten's pedotransfer functions were not very suitable to estimate the SWRC for the location studied.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm

Zhang

Wang

Luo

2018

Water

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This paper employs an optimization algorithm called the salp swarm algorithm (SSA) for the parameter estimation of the soil water retention curve model. The SSA simulates the behavior of searching for food of the salp swarm and manages to find the optimal solutions for optimization problems. In this paper, parameter estimation of the van Genuchten model based on nine soil samples, covering eight soil textures, is conducted. The optimization problem that minimizes the difference between the measured and the estimated water content is formulated, and the SSA is applied to solve this problem. To validate the competitive advantage of the SSA, the experimental results are compared with Particle Swarm Optimization algorithm, the Differential Evolution algorithm and the RETC program, which indicates that SSA performs better than the three methods.

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Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm

Zhang

Wang

Luo

2018

Water

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“…Then, the implementation of the technique in this work is discussed. The evolutionary algorithm called PSO was first proposed by Elberhat and Kennedy in 1995 to solve various real-valued optimization problems [ 15 , 40 ]. PSO is a powerful technique for achieving the best solution in a nonlinear system.…”

Section: Pi-based Pso Algorithmmentioning

confidence: 99%

“…The Jaya algorithm optimization is also introduced to tune the PID controller for minimizing frequency deviation in hybrid electric vehicle (HEV) applications [14]. One study implemented the Jaya algorithm optimization in the parameter estimation of the soil water retention to validate the effectiveness of the algorithm [15]. Similarly, the BAT algorithm has been utilized in another work [16] to search for the optimal parameters of the PI controller in a grid-connected PV system.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system

et al. 2020

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The lack of control in voltage overshoot, transient response, and steady state error are major issues that are frequently encountered in a grid-connected photovoltaic (PV) system, resulting in poor power quality performance and damages to the overall power system. This paper presents the performance of a control strategy for an inverter in a three-phase grid-connected PV system. The system consists of a PV panel, a boost converter, a DC link, an inverter, and a resistor-inductor (RL) filter and is connected to the utility grid through a voltage source inverter. The main objective of the proposed strategy is to improve the power quality performance of the three-phase grid-connected inverter system by optimising the proportional-integral (PI) controller. Such a strategy aims to reduce the DC link input voltage fluctuation, decrease the harmonics, and stabilise the output current, voltage, frequency, and power flow. The particle swarm optimisation (PSO) technique was implemented to tune the PI controller parameters by minimising the error of the voltage regulator and current controller schemes in the inverter system. The system model and control strategies were implemented using MATLAB/Simulink environment (Version 2020A) Simscape-Power system toolbox. Results show that the proposed strategy outperformed other reported research works with total harmonic distortion (THD) at a grid voltage and current of 0.29% and 2.72%, respectively, and a transient response time of 0.1853s. Compared to conventional systems, the PI controller with PSO-based optimization provides less voltage overshoot by 11.1% while reducing the time to reach equilibrium state by 32.6%. The consideration of additional input parameters and the optimization of input parameters were identified to be the two main factors that contribute to the significant improvements in power quality control. Therefore, the proposed strategy effectively enhances the power quality of the utility grid, and such an enhancement contributes to the efficient and smooth integration of the PV system.

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“…Compared with other optimization methods, Jaya obtained better results in terms of best, mean, and worst values of different unconstrained benchmark functions [16]. Similarly to TLBO, Jaya has been recently applied to optimizing a wide variety of engineering problems [31]- [38].…”

Section: B the Jaya Algorithmmentioning

confidence: 99%

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

et al. 2019

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The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed.

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Parameter estimation of the soil water retention curve model with Jaya algorithm

Cited by 26 publications

References 14 publications

Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm

Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm

Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

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