In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects ...and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn’t guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent metaheuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm.
•PPA based approach is proposed as EMS for hybrid PV/FC/battery/SC.•Hydrogen consumption minimization is selected as the target.•Comparison to SMC, WCA, DDAO, SHO, EEMS, MPA, and MA is conducted.•PPA based EMS robustness is confirmed with 95.34% efficiency and H2 consumption of 15.7559 gm.
Features Selection (FS) approaches have more attention since they have been applied to several fields primarily to deal with high dimensional data. An increase in the dimension of data can lead to ...degradation of the accuracy of the machine learning method. Therefore, there are several FS methods based on meta-heuristic (MH) techniques that have been developed to tackle the FS problem and avoid the limitations of traditional FS approaches. However, those MH methods still need improvements that suffer from some drawbacks that affect the quality of the final output. So, this paper proposed a modified Henry Gas Solubility Optimization (HGSO) using enhanced Harris hawks optimization (HHO) based on Heavy-tailed distributions (HTDs). In this study, a dynamical exchange between five HTDs is used to boost the HHO that modifies, in turn, the exploitation phase in HGSO. As a result, we proposed a dynamic modified HGSO based on enhanced HHO (DHGHHD). To assess the efficiency of the proposed DHGHHD, a set of eighteen UCI datasets are used. Furthermore, it applied to improve the prediction of two real-world datasets in the drug design and discovery field. The DHGHHD is compared with eight well-known MH methods. Comparison results illustrate the high quality of DHGHHD according to the values of accuracy, fitness value, and the number of selected features.
This paper proposes a reliable approach-based Harris Hawks Optimizer (HHO) to evaluate the optimal parameters of the Proportional–Integral (PI) controller simulating load frequency control (LFC) ...incorporated in a multi-interconnected system with renewable energy sources (RESs). During the optimization process, the integral time absolute error (ITAE) of the frequency and tie-line power is handled as the objective function. The HHO is selected due to its ease and requirement of less controlling parameters. Two different interconnected power systems are investigated as test systems to demonstrate the robustness of the proposed controller based HHO by comparing to other optimizers and traditional controller. The considered two systems include, one comprises two interconnected area of thermal and photovoltaic (PV) plants while the other system has four plants of PV, wind turbine (WT), and two thermal plants considering governor dead-band (GDB) and generation rate constraint (GRC). Different cases of load disturbance are studied, and the obtained results via the HHO are compared to Sine Cosine Algorithm (SCA), Multi-verse optimizer (MVO), Antlion Optimizer (ALO), and Grey wolf optimizer (GWO) as well as traditional controller. Moreover, sensitivity analysis is performed by changing the system parameters in a range of ± 10% and the performance of the proposed HHO-LFC is evaluated. The obtained results confirmed the reliability and superiority of the proposed approach based HHO in designing LFC for the considered systems.
The photovoltaic (PV) system operation faces great challenges as its performance depends on the weather conditions like irradiance and temperature. One of the phenomena that has negative effect on ...the PV array is operation under partial shade condition (PSC) as it causes hot spots, increases the power loss, and reduces the generated power. Therefore, this work proposes recent methodology incorporated metaheuristic approach named African vultures optimization algorithm (AVOA) that is applied for the first time to reconfigure the PV array operated at PSC for maximizing the generated power. The merit of AVOA is its high ability to escape from the local optima. Five shade patterns of short wide (SW), long wide (LW), short narrow (SN), long narrow (LN), and lower triangle are analyzed. Moreover, comparison to total cross tied (TCT), SudoKu, harris hawks optimizer (HHO), Aquila optimizer (AO), and antlion optimizer (ALO) is conducted. Furthermore, comparative analysis in terms of fill factor (FF), power enhancement (Pe) with respect to TCT arrangement, power loss, and performance ratio (PR) is conducted. The proposed AVOA outperformed the others in terms of the power enhancement and performance ratio. The best Pe obtained via the proposed AVOA is 39.91% in the fifth shade pattern while the best PR is 82.9125% in the third pattern. Additionally, Wilcoxon sign rank, Friedman, ANOVA table, and multiple comparison tests are performed. The results demonstrated that, AVOA results are significantly different from HHO over the five studied cases. The reported p-values based on Friedman and ANOVA illustrated the existence of significant differences among algorithms. The best p-values for Friedman and ANOVA are 9.4725e−08 and 7.3013e−13 in the fourth and fifth patterns respectively. The results confirmed the preference of the proposed AVOA in achieving the best reconfiguration of the PV array at PSC. Chaotic mapping is recommended to adaptively set the proposed AVOA parameters.
A partial shading condition is an environmental phenomenon that causes multiple peaks in Photovoltaic (PV) characteristics. Introducing robust and reliable Maximum Power Point Tracking technique is ...essential in PV systems to extract the Global Maximum Power Point (GMPP) irrespective of the environmental conditions. Therefore in this manuscript, a novel optimization algorithm is implemented for MPPT. The developed technique named Chaotic Flower Pollination Algorithm (C-FPA) merges the chaos maps (Logistic, sine, and tent maps) to tune the basic algorithm parameters adaptively. The effectiveness of the introduced variants is proved using several patterns of partial shading condition. Moreover, these variants are certified for tracking the GMPP in case of dynamic and sudden variation in the irradiance conditions. Several statistical analysis is carried out to evaluate the performance of the proposed variants in comparison with the standard version of the Flower Pollination Algorithm (FPA). The significant outcome clarifies that combining the chaos maps with FPA improves the dependability and stability of the FPA and offers higher tracking efficiency with a reduction of tracking time by 50% when compared to FPA. Moreover, the proposed C-FPA provides a better dynamic response, especially with the tent chaos map.
One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point ...(MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This article proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across <inline-formula> <tex-math notation="LaTeX">9 \times 9 </tex-math></inline-formula> PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels.
•This paper deals with state of the art of various static and dynamic reconfiguration techniques for avoiding power losses due to partial shading.•Challenges in implementing various reconfiguration ...techniques to attain maximum power in PV systems are discussed.•Comparative analysis between various reconfiguration techniques based on various performance parameters is elucidated.•Energy saving and income generation analysis are also outlined for assessment.•Future scope is presented to give path to new researchers to work in this field.
Environmental conditions have a strong influence on the behavior and quality of the generated photovoltaic (PV) power. The partial sharing condition is a natural phenomenon that reduces the lifetime of the PV array and the effectiveness of the entire system. Dispersing the shadow equally over PV panels is the solution to avoid the negative impact of the partial shading condition. Accordingly, PV array reconfiguration has become an attractive topic of study for researchers. In this paper, the authors provide a comprehensive review of all the schemes proposed for the PV reconfiguration system. In addition, a comparative study among all the published approaches and the challenges facing each approach are presented. Future research topics that highlight new methods to produce higher power from an available PV system are discussed.
Enhancing the exploration and exploitation phases of the metaheuristic (MH) optimization algorithms is the key to avoiding local optima. The Manta ray foraging optimizer is a recently proposed MH ...optimizer. The MRFO showed a good performance in the simple optimization problems. However, it is trapped into the local optimum in the more elaborated ones due to the original algorithm’s low capability in exploiting the optimal solutions and its convergence. From this principle, in this work, a novel variant of the Manta ray foraging optimizer has been proposed for global optimization problems, engineering design optimization problems, and multi-threshold segmentation. In the proposed approach, the fractional calculus (FC) using Caputo fractional differ-sum operator has been adopted to enhance the manta rays movement in the exploitation phase via utilizing history dependency of FC to boost exploiting the optimal solutions via sharing the past knowledge over the optimization process. Moreover, to avoid premature convergence, the somersault factor has been adaptively tuned. The fractional-order Caputo Manta-Ray Foraging Optimizer (FCMRFO) has been proposed. The proposed algorithm’s sensitivity for the FC coefficients has been tested with ten-dimensional CEC2017 benchmarks. The scalability test of the proposed algorithm has been performed with 30, 50 and 100-dimensional CEC2017. Moreover, CEC2020 benchmarks with dimensions 5 and 20 have been applied for providing an extensive investigation, and the FCMRFO has been compared with recent state-of-the-art algorithms. Through utilizing the non-parametric statistical analysis and ranking test, the FCMRFO confirms its superiority and ability to avoid the local optimum in several cases. For the second part of the study, three constrained engineering design problems have been investigated; then, numerous natural images are applied to appraise the FCMRFO for multilevel threshold image segmentation. By performing several metrics, the FCMRFO proves its quality and efficiency compared to recent well-regarded algorithms in engineering applications and image segmentation.
•Novel memory-based fractional-order Caputo Manta ray foraging optimizer is proposed.•Adaptively tuned somersault factor is used for balancing memory window and solutions.•The proposed optimizer validated with CEC2017 and CEC2020 with several dimensions.•The FCMRFO applicability is confirmed with engineering applications and segmentation.•Comparison of the state-of-the-art techniques using non-parametric tests is performed.
Several metaheuristic methods have been applied to tackling various global and engineering optimization problems. However, this method still needs more improvement since they require a suitable ...balance between exploration and exploitation. Therefore, this study presents an enhancement of the arithmetic optimization algorithm (AOA) as a global optimization method. The developed method, named AOASC, depends on using the sine-cosine algorithm’s operators to enhance the exploitation ability of AOA during the searching process. This leads to improving the convergence rate of the developed method toward the optimal solution. Besides, improve the process of avoiding the attraction toward the local point. Besides these behaviors, the quality of the final solution (best one) is improved. To validate the efficiency of the developed method, a set of experiments is conducted, including various optimization problems, such as ten benchmark functions and five engineering optimization problems. Besides, the results of the developed method are compared with other well-known metaheuristic methods. The results showed the high efficiency of the developed method over other methods in terms of performance measures.
Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important ...steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.
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