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

Zhang, Jing; Wang, Zhenhua; Luo, Xiong

doi:10.3390/w10060815

Cited by 59 publications

(20 citation statements)

References 29 publications

(34 reference statements)

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“…e expression we propose meets this requirement F − 1 S (proposed) (0) � 0, and the excellent performance will be presented in Section 5. [14] and has been used in several aspects including wireless sensor networks [15], feature selection [16][17][18], parameter estimation [19], and clustering [20]. Salps usually exist in the form of a swarm called salp chain in deep oceans.…”

Section: Expression For Approximating Nakagami-m Quantilementioning

confidence: 99%

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami-mdistribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami-mquantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami-mquantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami-mquantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami-mquantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.

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Section: Expression For Approximating Nakagami-m Quantilementioning

confidence: 99%

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Gao

Hou

Zhang

et al. 2019

Mathematical Problems in Engineering

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“…The SSA algorithm has been applied to various optimization problems because of its simple mechanism, dynamic nature and strong global search ability. Such as extracting the parameters of photovoltaic system cell [63,64], optimization of softwaredefined networks [65], train neural networks [66,67], optimize parameters of soil water retention [68], tariff optimization in electrical systems [69], image segmentation [70,71], target localization [72], optimal power flow problem [73], estimation of optimal parameters of polymer exchange membrane fuel cells [74], feature selection [75,76,77,78,79,80] and others. Although the SSA algorithm is very competitive, it still suffers from some limitations such as poor convergence, unbalanced exploration and exploitation capacities, which may lead to local optimum stagnation when solving some intractable optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization

et al. 2021

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Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmetaheuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.

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“…Salp swarm algorithm (SSA), which was proposed by Mirjalili et al in 2017, is an efficient meta-heuristic optimization algorithm that mimics the swarming behavior and the predation model of salp swarm [25,26]. The algorithm, which possesses a simple program structure and fast computation, has been used in many project areas, such as load frequency control [27], the design of IIR wideband digital differentiators and integrators [28], parameter estimations for soil-water retention curves [29], interval prediction for short-term load forecasting [30], and the quality enhancement of an islanded microgrid [31].…”

Section: Introductionmentioning

confidence: 99%

Proportional–Integral–Derivative Controller Design Using an Advanced Lévy-Flight Salp Swarm Algorithm for Hydraulic Systems

et al. 2020

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To improve the control ability of proportional–integral–derivative (PID) controllers and increase the stability of force actuator systems, this paper introduces a PID controller based on the self-growing lévy-flight salp swarm algorithm (SG-LSSA) in the force actuator system. First, the force actuator system model was built, and the transfer function model was obtained by the identification of system parameters identifying. Second, the SG-LSSA was proposed and used to test ten benchmark functions. Then, SG-LSSA-PID, whose parameters were tuned by SG-LSSA, was applied to the electro-hydraulic force actuator system to suppress interference signals. Finally, the temporal response characteristic and the frequency response characteristic were studied and compared with different algorithms. Ten benchmark function experiments indicate that SG-LSSA has a superior convergence speed and perfect optimization capability. The system performance results demonstrate that the electro-hydraulic force actuator system utilized the SG-LSSA-PID controller has a remarkable capability to maintain the stability and robustness under unknown interference signals.

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

Cited by 59 publications

References 29 publications

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

An Efficient Approximation for Nakagami-mQuantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization

Proportional–Integral–Derivative Controller Design Using an Advanced Lévy-Flight Salp Swarm Algorithm for Hydraulic Systems

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