A
            <scp>MPPT</scp>
            based on optimized
            <scp>FLC</scp>
            using manta ray foraging optimization algorithm for thermo‐electric generation systems

Aly, Mokhtar; Rezk, Hegazy

doi:10.1002/er.6728

Cited by 23 publications

(15 citation statements)

References 47 publications

(44 reference statements)

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“…Manta ray foraging optimization (MRFO) is a new swarm intelligence optimization algorithm proposed in 2020. With excellent searchability, fewer parameters, simple model and easily understood, it is better than particle swarm optimization (PSO) [ 11 , 12 ], genetic algorithm (GA) [ 13 , 14 ], Differential Evolution (DE) [ 15 , 16 ], Cuckoo Search (CS) [ 17 ], gravitational search algorithm (GSA) [ 18 ], and ABC [ 19 ] in some function optimization [ 20 ], and it has been successfully applied to solar energy [ 21 , 22 ], ECG [ 23 ], generator [ 24 , 25 ], power system [ 26 ], cogeneration energy system [ 27 ], geophysical inversion problem [ 28 ], directional overcurrent relay [ 29 ], feature selection [ 30 ], hybrid energy system [ 31 ], and sewage treatment [ 32 ]. Although MRFO has good optimization ability, it still has its own defects.…”

Section: Introductionmentioning

confidence: 99%

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu

Xie

Zhou

2022

Computational Intelligence and Neuroscience

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Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

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Section: Introductionmentioning

confidence: 99%

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu

Xie

Zhou

2022

Computational Intelligence and Neuroscience

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“…An optimized type-1 FL-based tracker for thermoelectric generation application has been proposed by Aly and Rezk. 19 The optimal parameters of type-1 FL have been determined by manta ray foraging optimization. A new MPPT method for TEGs based on power differentials has been proposed by Yahya and Alomari.…”

Section: Introductionmentioning

confidence: 99%

“…The main results showed the efficiency of that MPPT to maximize the energy harvesting of the TEGS. An optimized type‐1 FL‐based tracker for thermoelectric generation application has been proposed by Aly and Rezk 19 . The optimal parameters of type‐1 FL have been determined by manta ray foraging optimization.…”

Section: Introductionmentioning

confidence: 99%

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A robust type‐2 fuzzy logic‐based maximum power point tracking approach for thermoelectric generation systems

Rezk

Harrag

2021

Intl J of Energy Research

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A robust type-2 fuzzy logic (FL)-based approach for maximum power point tracking (MPPT) is proposed in this work. The method is used to increase the energy efficiency of thermoelectric generators (TEGs). Type-2 FL was applied to adapt the variable step size of incremental resistance (INR) MPPT to track the maximum available power of a TEG. The output power from a TEG relies mostly on the difference in temperature of its two sides added to the load value. Consequently, MPPT must be robust to extract the optimum operation point continually under varying operational conditions. The aim of the

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“…In this study, a stochastic algorithm, namely Barnacle Mating Optimization algorithm (BMO), is suggested as a means to finding the global maximum power point of a thermos electric generator. The control is achieved by varying the duty cycle of the boost converter according to the results of the BMO algorithm [38]. The main features of BMO are listed below:…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

et al. 2021

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The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.

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A MPPT based on optimized FLC using manta ray foraging optimization algorithm for thermo‐electric generation systems

Cited by 23 publications

References 47 publications

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

A robust type‐2 fuzzy logic‐based maximum power point tracking approach for thermoelectric generation systems

Optimal Control of Centralized Thermoelectric Generation System under Nonuniform Temperature Distribution Using Barnacles Mating Optimization Algorithm

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