Novel Manta Rays Foraging Optimization Algorithm Based Optimal Control for Grid-Connected PV Energy System

Alturki, Fahd A.; Omotoso, Hammed Olabisi; Al-Shamma’a, Abdullrahman A.; Farh, Hassan M. H.; AlSharabi, Khalil

doi:10.1109/access.2020.3030874

Cited by 43 publications

(19 citation statements)

References 36 publications

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“…For instance, the authors in [49] have examined the global maximum power point (GMPP) of partially shaded MJSC PV array applying the MRFO algorithm. In addition, fahd et al [50] applied the standard MRFO to perform the dynamic operation for connecting PV into the grid system. Regarding the work of Selem et al [51], the MRFO was applied to define the unknown electrical parameters of proton exchange membrane fuel cells (PEMFC) stacks, which is considered as a constrained optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

et al. 2022

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Manta ray foraging optimization (MRFO) algorithm is relatively a novel bio-inspired optimization technique directed to given real-world engineering problems. In this present work, wind turbines layout (WTs) inside a wind farm ,is considered as a real nonlinear optimization problem. In spite of the better convergence of MRFO, it gets stuck into local optima for large problems. The chaotic sequences are among the performed techniques used to tackle this shortcoming and improve the global search ability. Therefore, ten chaotic maps have been embedded into MRFO. To affirm the performance of the suggested chaotic approach CMRFO, it was first assessed using the IEEE CEC-2017 benchmark functions. This examination has been systematically compared to eight well-known optimization algorithms and the original MRFO. The non-parametric Wilcoxon statistical analysis significantly demonstrates the superiority of CMRFO as it ranks first in most test suites. Secondly, the MRFO and its best enhanced chaotic version were tested on the complex problem of finding the optimal locations of wind turbines within a wind farm. Besides, The application of the CMRFO to the wind farm layout optimization (WFLO) issue aims to minimize the cost per unit power output, and increase the electrical power engendered by all WTs and wind-farm efficiency. Two representative scenarios of the problem have been dealt with a square-shaped farm installed on an area of 2x2km, including variable wind direction with steady wind speed, and both wind direction and speed are variable. The WFLO outcomes reveal the CMRFO capability to find the optimal locations of WTs, that generate a maximum power for a minimum cost compared to three stochastic approaches and other previous studies. At last, the suggested CMRFO with Singer chaotic sequence has been successfully enhanced by accelerating the convergence, and providing better accuracy to find the global optimum.

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Section: Introductionmentioning

confidence: 99%

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

et al. 2022

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“…Shaheen et al [30] optimized the MRFO algorithm by using adaptive penalty parameters and introduced it to handle a thermal scheduling problem. In addition, MRFO has been extended to many fields, such as feature selection (FS) [31], fuel power generation [32], system identification [33], photovoltaic system operation [34], and other areas.…”

Section: Introductionmentioning

confidence: 99%

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Duan

Liu

et al. 2021

Engineering with Computers

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The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

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“…The performance of the BO algorithm is extensively compared to MRFO [41,42], ABO [36], PSO [27], FPA [28] and SDO [32].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

et al. 2021

Self Cite

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In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

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Novel Manta Rays Foraging Optimization Algorithm Based Optimal Control for Grid-Connected PV Energy System

Cited by 43 publications

References 36 publications

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

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