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A Novel Improved gradient-based optimizer for Single-Sensor Global MPPT of PV System

JOURNAL OF MATHEMATICS 2025年第1期2025卷

作者： Rezk, Hegazy Issa, Usama Hamed Bouaouda, Anas Hashim, Fatma A. Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Minia Univ Fac Engn Dept Elect Engn Al Minya Egypt Minia Univ Fac Engn Dept Civil Engn Al Minya Egypt Taif Univ Coll Engn Dept Civil Engn Taif Saudi Arabia Hassan II Univ Casablanca Fac Sci & Technol Mohammadia Morocco Helwan Univ Fac Engn Helwan Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan

gradient-based optimizer (GBO) is a highly mathematics-based metaheuristic algorithm that has garnered significant attention since its introduction. It offers several inherent advantages, such as low computational complexity, rapid convergence, and easy implementation. However, GBO has some drawbacks, including a lack of population diversity and a tendency to get trapped in local optima. To address these shortcomings, this research introduces an improved version of GBO (iGBO). In iGBO, introducing the Sobol sequence strategy ensures a higher-quality initial population and enhances the convergence speed. Additionally, a new modified Local Escaping Operator (LEO) is proposed, which incorporates the sine-cosine operator and the DCS/Xbest/Current-to-2rand strategy. This modified LEO improves the optimization efficiency of GBO and boosts its local search capability, helping to avoid local optima. The superiority of iGBO is thoroughly verified through comparisons with the original GBO and several well-known and newly developed algorithms on the IEEE CEC'2022 benchmark suite. Furthermore, the proposed approach is applied to extract the photovoltaic system's global maximum power point (MPP) under shading conditions. Three different shading patterns are considered to assess the reliability of iGBO. The performance of the developed iGBO is compared with several leading algorithms, including Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Black Widow Optimization Algorithm (BWOA), Pelican OA (POA), Chimp OA (ChOA), Osprey OA (OOA), and the original GBO. The results reveal that iGBO-based MPPT consistently outperforms its competitors in identifying the global MPP under various shading conditions followed by PSO, while RSA performs the least effectively.

关键词： gradient-based optimizer metaheuristics MPPT optimization photovoltaic single sensor

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gradient-based optimizer for Parameter Extraction in Photovoltaic Models

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IEEE ACCESS 2021年 9卷 13403-13416页

作者： Ismaeel, Alaa A. K. Houssein, Essam H. Oliva, Diego Said, Mokhtar Arab Open Univ AOU Fac Comp Studies FCS Muscat 130 Oman Menia Univ Fac Sci Al Minya 61519 Egypt Menia Univ Fac Comp & Informat Al Minya 61519 Egypt Univ Oberta Catalunya Comp Sci Dept IN3 Castelldefels 08860 Spain Univ Guadalajara Dept Ciencias Computac CUCEI Guadalajara 44430 Mexico Fayoum Univ Elect Engn Dept Fac Engn Faiyum 63514 Egypt

Solar radiation is increasingly used as a clean energy source, and photovoltaic (PV) panels that contain solar cells (SCs) transform solar energy into electricity. The current-voltage characteristics for PV models is nonlinear. Due to a lack of data on the manufacturer's datasheet for PV models, there are several unknown parameters. It is necessary to accurately design the PV systems by defining the intrinsic parameters of the SCs. Various methods have been proposed to estimate the unknown parameters of PV cells. However, their results are often inaccurate. In this article, a gradient-based optimizer (GBO) was applied as an efficient and accurate methodology to estimate the parameters of SCs and PV modules. Three common SC models, namely, single-diode models (SDMs), double-diode models (DDMs), and three-diode models (TDMs) were used to demonstrate the capacity of the GBO to estimate the parameters of SCs. The proposed GBO algorithm for estimating the optimal values of the parameters for various SCs models are applied on the real data of a 55 mm diameter commercial R.T.C-France SC. Comparison between the GBO and other algorithms are performed for the same data set. The smallest value of the error between the experimental and the simulated data is achieved by the proposed GBO. Also, high closeness between the simulated P-V and I-V curves is achieved by the proposed GBO compared with the experimental.

关键词： Optimization Semiconductor diodes Time division multiplexing Equivalent circuits Renewable energy sources Photovoltaic systems Mathematical model gradient-based optimizer photovoltaic modules diode models renewable energy solar energy

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gradient-based optimizer (GBO): A Review, Theory, Variants, and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2023年第4期30卷 2431-2449页

作者： Daoud, Mohammad Sh. Shehab, Mohammad Al-Mimi, Hani M. Abualigah, Laith Zitar, Raed Abu Shambour, Mohd Khaled Yousef Al Ain Univ Abu Dhabi U Arab Emirates Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Al Zaytoonah Univ Dept Cybersecur Amman Jordan Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia Ho Chi Minh City Open Univ Ctr Engn Applicat & Technol Solut Ho Chi Minh Vietnam Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates Umm Al Qura Univ Custodian Two Holy Mosques Inst Hajj & Umrah Res Mecca Saudi Arabia

This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into;GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

关键词： gradient-based optimizer Optimization algorithms Engineering problems GBO's variants GBO's applications

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gradient-based optimizer for economic optimization of engineering problems

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MATERIALS TESTING 2022年第5期64卷 690-696页

作者： Mehta, Pranav Yildiz, Betul Sultan Sait, Sadiq M. Yildiz, Ali Riza Bursa Uludag Univ Uludag Univ Dept Mech Engn TR-16059 Bursa Turkey Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India Bursa Uludag Univ Dept Elect & Energy Bursa Turkey King Fahd Univ Petr & Minerals Dhahran Saudi Arabia

Optimization of the heat recovery devices such as heat exchangers (HEs) and cooling towers is a complex task. In this article, the widely used fin and tube HE (FTHE) is optimized in terms of the total costs by the novel gradient-based optimization (GBO) algorithm. The FTHE s have a cylindrical tube with transverse or longitudinal fin enhanced on it. For this study, various constraints and design variables are considered, with the total cost as the objective function. The study reveals that the GBO provides promising results for the present case study with the highest success rate. Also, the comparative results suggest that GBO is the robust optimizer in terms of the best-optimized values of the fitness function vis-a-vis design variables. This study builds the future implications of the GBO in a wide range of engineering optimization fields.

关键词： fin and tube heat exchanger gradient-based optimizer metaheuristic optimum design

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Blockchain-Assisted Secure Smart Home Network Using gradient-based optimizer With Hybrid Deep Learning Model

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IEEE ACCESS 2023年 11卷 86999-87008页

作者： Almuqren, Latifah Mahmood, Khalid Aljameel, Sumayh S. Salama, Ahmed S. Mohammed, Gouse Pasha Alneil, Amani A. Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Art Mohayil Dept Informat Syst Abha 61421 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Dept Comp Sci Dammam 31441 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

The Internet of Things (IoT) refers to a technology enabler to enhance the urban physical architecture and render public services. But, public access to accumulated heterogeneous IoT urban information is prone to hackers attacking connected devices to the internet intellectual property as well. IoT security serves a dynamic part in the smart city. Some IoT devices are connected in smart homes, and these connections were centred on gateways. In smart homes, the gateways gain a lot of significance;but their centralized structure causes many security vulnerabilities like availability, integrity, and certification. Unified "cloud-like" computing networks and Blockchain (BC) type systems should be used to sort out these problems. Therefore, this article develops a Blockchain-Assisted Secure Smart Home Network using gradient based optimizer with Hybrid Deep Learning (BSSHN-GBOHDL) model. The presented BSSHN-GBOHDL technique employs BC technology to improve the confidentiality of the data in the smart home environment. In addition, the BSSHN-GBOHDL technique identifies malicious activities in the smart home environment via three sub-processes namely data preprocessing, hybrid deep learning (HDL)-based malicious activity classification, and GBO-based hyperparameter tuning. The GBO algorithm assists in the proficient hyperparameter selection of the HDL model, which aids in accomplishing increased detection efficiency. The experimental validation of the BSSHN-GBOHDL approach is tested on a benchmark NSL-KDD dataset with 65495 normal and 60743 attack samples. The results highlight the betterment of the BSSHN-GBOHDL approach over other recent methods with maximum accuracy of 98.29%.

关键词： Smart homes Logic gates Deep learning Computer architecture Blockchains Computational modeling Tuning Internet of Things Network security gradient methods blockchain network security deep learning gradient-based optimizer

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Clustering-assisted gradient-based optimizer for scheduling parallel cloud workflows with budget constraints

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JOURNAL OF SUPERCOMPUTING 2024年第12期80卷 17097-17134页

作者： Li, Huifang Chen, Boyuan Huang, Jingwei Song, Zhuoyue Xia, Yuanqing Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China China Air To Air Missile Luoyang 471009 Peoples R China

Cloud computing has gradually become one of the most popular platforms for executing scientific applications due to its elastic and on-demand resource provisional capabilities. But, how to effectively schedule a set of parallel workflows to minimize the makespan under their individual budget constraints remains a critical problem. This work proposes a Clustering-assisted gradient-based optimizer (C-GBO) to improve the performance for scheduling workflows in cloud environments. First, it designs a novel individual encoding mechanism including task-VM mapping and task-priority sub-strings to further optimize the makespan by updating both sub-strings simultaneously, especially each element representing task execution order in a task-priority sub-string can take any values within the pre-specified range but not subject to control dependencies among tasks. Second, to address the original GBO's easiness of falling into local optima brought by its only one best guiding solution, it divides individuals into different groups as their position information by the K-means algorithm and selects the best guiding solution for each group with 50% probability from their own clusters locally, such that the search diversity is improved and the cross evolution is reduced. Third, a Gaussian disturbance-based elite enhancement strategy is developed by introducing a Gaussian disturbance operation to a certain number of each elite individual so as to fully exploit these individuals and increase the quality of the global best solution. Our proposed C-GBO algorithm is testified and compared with six peers on datasets with different scales through WorkflowSim. The results demonstrate that our proposed C-GBO achieves the best results in all the involved algorithms, i.e., obtaining the success rate of 100% on all datasets, and performs better in the average makespan that is at most 88.47% shorter than its peers in most cases.

关键词： Cloud computing Meta-heuristics Workflow Scheduling gradient-based optimizer

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Performance of gradient-based optimizer for Optimum Wind Cube Design

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Computers, Materials & Continua 2022年第4期71卷 339-353页

作者： Alaa A.K.Ismaeel Essam H.Houssein Amir Y.Hassan Mokhtar Said Faculty of Computer Studies(FCS) Arab Open University(AOU)Muscat130Sultanate of Oman Faculty of Science Minia UniversityMiniaEgypt Faculty of Computers and Information Minia UniversityMiniaEgypt Electronics Research Institute GizaEgypt Electrical Engineering Department Faculty of EngineeringFayoum UniversityFayoumEgypt

Renewable energy is a safe and limitless energy source that can be utilized for heating,cooling,and other *** energy is one of the most important renewable energy *** fluctuation of wind turbines occurs due to variation of wind velocity.A wind cube is used to decrease power fluctuation and increase the wind turbine’s *** optimum design for a wind cube is the main contribution of this *** decisive design parameters used to optimize the wind cube are its inner and outer radius,the roughness factor,and the height of the wind turbine hub.A gradient-based optimizer(GBO)is used as a new metaheuristic algorithm in this *** objective function of this research includes two parts:the first part is to minimize the probability of generated energy loss,and the second is to minimize the cost of the wind turbine and wind *** gradient-based optimizer(GBO)is applied to optimize the variables of two wind turbine types and the design of the wind *** metrological data of the Red Sea governorate of Egypt is used as a case study for this *** on the results,the optimum design of a wind cube is achieved,and an improvement in energy produced from the wind turbine with a wind cube will be compared with energy generated without a wind *** energy generated from a wind turbine with the optimized cube is more than 20 times that of a wind turbine without a wind cube for all cases studied.

关键词： Wind turbine wind cube gradient-based optimizer metaheuristics energy source

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An enhanced gradient-based optimizer for parameter estimation of various solar photovoltaic models

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ENERGY REPORTS 2022年 8卷 15249-15285页

作者： Premkumar, M. Jangir, Pradeep Ramakrishnan, C. Kumar, C. Sowmya, R. Deb, Sanchari Kumar, Nallapaneni Manoj Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bengaluru 560078 Karnataka India Rajasthan Rajya Vidyut Prasaran Nigam Sikar 332025 Rajasthan India SNS Coll Technol Dept Elect & Elect Engn Coimbatore 641035 Tamil Nadu India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur 639113 Tamil Nadu India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England City Univ Hong Kong Sch Energy & Environm Kowloon Hong Kong Peoples R China Graph Era Deemed Be Univ Dept Elect Engn Dehra Dun 248002 Uttarakhand India HICCER Hariterde Int Council Circular Econ Res Ctr Res & Innovat Sci Technol Engn Arts & Math St Palakkad 678631 Kerala India

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current-voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the gradient-based optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder-Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed;therefore, the proposed algorithm is called a Criss-Cross-based Nelder-Mead simplex gradient-based optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E-04, case 2 is 2.43E-04, and the average integral absolute error and relative error values are 1.05E-02 and 3.51E-03, respectively, for all case studies. With Friedman's rank

关键词： Criss-cross algorithm gradient-based optimizer Nelder-Mead simplex Parameter estimation Solar photovoltaic models

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Hybrid Generalized Regularized Extreme Learning Machine Through gradient-based optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport

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BIG DATA 2024年第4期12卷 282-298页

作者： Gul, Enes Safari, Mir Jafar Sadegh Inonu Univ Dept Civil Engn Malatya Turkiye Yasar Univ Dept Civil Engn TR-35100 Izmir Turkiye

Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.

关键词： extreme learning machine generalized regularized extreme learning machine gradient-based optimizer particle swarm optimization sediment transport self-cleansing

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Improved gradient-based optimizer for parameters extraction of photovoltaic models

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IET RENEWABLE POWER GENERATION 2022年第8期16卷 1602-1622页

作者： Jiang, Yugui Luo, Qifang Zhou, Yongquan Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China

An efficient operation of the solar photovoltaic (PV) system relies on accurate and reliable equivalent models and parameters. For different modular circuit models, the parameter estimation of PV solar cells is a key task that is usually translated into optimization problems solved by metaheuristic algorithms. gradient-based optimizer (GBO), which was proposed in 2020, is a method with swarm characteristics that was developed from Newton's method. gradient search rule (GSR) and local escape operator (LEO) is main components of the GBO algorithm. This paper proposes an improved GBO (IGBO) optimization algorithm based on the original algorithm. In IGBO, two strategies, adaptive weights and chaotic behaviour, are introduced to adjust the adaptive parameters. The purpose of adaptive weights is to effectively approach the optimal solution and avoid falling into local optimum in different search phases of the algorithm. Chaotic behaviour aims to replace the randomness of the metaheuristic algorithm and to improve the convergence speed and accuracy of the algorithm. To validate the performance of IGBO, it is applied to the parameter extraction of different PV models. The PV models used in this paper include single diode model (SDM), double diode model (DDM) and PV module model. Finally, comparing with the original algorithm and six well-established algorithms, the experimental data show that the IGBO algorithm is highly competitive, as reflected by the best results obtained in terms of root mean square error values. It can be concluded that IGBO is more competitive in robustness, convergence and accuracy.

关键词： photovoltaic power systems IGBO algorithm solar photovoltaic system different search phases different PV models chaotic behaviour adaptive weights optimisation gradient-based optimizer single diode model particle swarm optimisation parameter extraction well-established algorithms optimal solution parameter estimation optimization problems metaheuristics metaheuristic algorithm GBO algorithm solar cells adaptive parameters GBO optimization algorithm parameters extraction Newton's method photovoltaic models search problems PV solar cells gradient methods different modular circuit models local escape operator reliable equivalent models mean square error methods

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