A hybrid of bio-inspired algorithm based on Levy flight and particle swarm optimizations for photovoltaic system under partial shading conditions

Charin, Chanuri; Ishak, Dahaman; Zainuri, Muhammad Ammirrul Atiqi Mohd; Ismail, B.; Jamil, Mohamad Kamarol Mohd

doi:10.1016/j.solener.2021.01.049

Cited by 50 publications

(21 citation statements)

References 50 publications

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“…Levy flight is a kind of random search that relies on Levy distribution, which has been applied many times in the optimization field in recent years. Levy flight is able to improve cuckoo and particle swarm optimization algorithms [23][24][25] and so on. LWOA owns a faster convergence speed and higher convergence accuracy; LWOA has a better ability to jump out of the local optimum.…”

Section: Principle Of Lwoamentioning

confidence: 99%

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

Zhang

Sheng

et al. 2022

Computational Intelligence and Neuroscience

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The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.

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Section: Principle Of Lwoamentioning

confidence: 99%

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

Zhang

Sheng

et al. 2022

Computational Intelligence and Neuroscience

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“…Recently, considerable research has revealed a strong desire in bioinspired MPPTs, which have outperformed intelligent methods such as the PSO [12], artificial-bee colony [13], grey-wolf-optimization technique [14], firefly algorithm [15], salp-swarm optimization [16], ant-colony optimization [17], and cuckoo-search algorithm [18] in various environmental conditions. Moreover, several research works have incorporated these methods into hybrid ones to improve their performance and eliminate their drawbacks, specifically, by integrating the optimization ability of various searching mechanisms into an incorporated form of at least two methods, to recover the limitations of one method via the performance of another [19].…”

Section: Introductionmentioning

confidence: 99%

African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

et al. 2022

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An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.

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“…The proposed method provides better results considering different PSCs; however, it is limited to multiconverter PV production systems. The particle swarm optimization (PSO) algorithm was implemented for tracking the global MPP (GMPP) considering the PSCs (Charin et al, 2021). The applicatins of the evoluationary algorithms in improving the performance of MPPT had been significantly increasing (Ahmed et al, 2022;Fan and Ma, 2022;Lousuwankun and Jantharamin, 2022;Rajesh et al, 2022;Wasim et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

Alanazi

Memon

et al. 2022

Front. Energy Res.

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In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using a hill climbing (HC)–artificial electric field algorithm (AEFA) considering a DC/DC buck converter. The AEFA is inspired by Coulomb’s law of electrostatic force and has a high speed and optimization accuracy. Because the traditional HC method cannot perform global search tracking and instead performs local search tracking, the AEFA is used for a global search in the proposed HC-AEFA. The critical advantage of the HC-AEFA is that it is desirable performing local and global searches. The proposed hybrid method is implemented to derive an MPP by tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power. Its capability is evaluated and compared with well-known particle swarm optimization (PSO), considering standards, PSCs, and radiation changes conditions. The tracking efficiency for the most challenging shading pattern (third pattern) using the HC-AEFA, HC, AEFA and PSO is obtained at 99.93, 90.35, 98.85, 99.80%, respectively. The analysis of the population-based optimization process for different algorithms proved the HC-AEFA faster convergence at lower iterations than the other methods. So, the superiority of the proposed HC-AEFA subjected to different patterns is confirmed with higher tracking efficiency and global power peak, fewer fluctuations, higher convergence speed, and higher dynamic and Static-efficiency compared to the other methods.

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A hybrid of bio-inspired algorithm based on Levy flight and particle swarm optimizations for photovoltaic system under partial shading conditions

Cited by 50 publications

References 50 publications

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients

African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

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