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Optimized controllers for stabilizing the frequency changes in hybrid wind-photovoltaic-wave energy-based maritime microgrid systems

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APPLIED ENERGY 2024年 361卷

作者： Peddakapu, K. Mohamed, M. R. Srinivasarao, P. Licari, J. Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Pekan Malaysia Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Tech Pekan Malaysia Univ Malta Fac Engn Dept Elect Engn MSD-2080 Msida Malta

Reducing the dependence on traditional energy sources and shifting towards the utilization of renewable energy sources (RES) of energy in the maritime sector is imperative for reducing greenhouse gas emissions. Inherently, RES sources like solar and wind are intermittent and variable, resulting in inconsistent power availability and hence leading to energy supply fluctuations and potential shortages. In this respect, an efficient control strategy to maintain system stability and address intermittency effectively is essential. This work considers a hybrid marine microgrid with various energy sources like photovoltaics (PV), wind energy conversion system (WECS), marine biodiesel generator, archimedes wave power generation, solid oxide fuel cell, and batteries. A 2-degree of freedom (2DOF) structure is designed and implemented with the tilt-integral-derivative filter (TIDN) to address frequency variations. Furthermore, an archimedes optimization algorithm (AOA) is used to optimize the 2DOFTIDN controller. The stability of the proposed microgrid system is assessed under various combinations of RES availabilities, including real-time data from WECS and PV. The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. Simulation results obtained show that the AOA-based 2DOF-TIDN control strategy achieves shorter settling times in mitigating the changes of marine microgrid systems under different dynamic conditions as compared to the other algorithms. Finally, the controller being proposed in this paper was tested for robustness with parameter deviations of +25%, -20%, and - 40% from the nominal values, and proved to be the proposed 2DOF-TIDN controller parameters demonstrate significant robustness in effectively managing the uncertainties and parametric variations.

关键词： Renewable energy sources Climate change Marine microgrid Energy storage units archimedes optimization algorithm

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archimedes Optimizer: Theory, Analysis, Improvements, and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2023年第4期30卷 2543-2578页

作者： Dhal, Krishna Gopal Ray, Swarnajit Rai, Rebika Das, Arunita Midnapore Coll Autonomous Dept Comp Sci & Applicat Midnapore W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Gangtok Sikkim India

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. archimedes optimization algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, archimedes optimization algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired optimization algorithms (PIOA), the archimedes optimization algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

关键词： Nature-inspired optimization algorithms Image segmentation optimization Tsallis t-entropy archimedes optimization algorithm Physics inspired optimization algorithms

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An archimedes metaheuristic algorithm based optimum relay coordination in microgrid and combined overhead/cable distribution network

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JOURNAL OF SUPERCOMPUTING 2023年第18期79卷 21166-21184页

作者： Kudkelwar, Sagar B. Sinha, Bam Bahadur Gunturi, Sravan Kumar GH Raisoni Inst Engn & Technol Elect Engn Dept Nagpur India Indian Inst Informat Technol Comp Sci & Engn Ranchi India Deemed Univ Koneru Lakshmaiah Educ Fdn Elect & Commun Engn Dept Hyderabad 500075 Telangana India

The issue of power system protection remains a crucial aspect for power engineers. One of the important protection devices in distribution networks is the overcurrent relay, which requires constant attention to ensure its performance is optimized. This paper applied very first time the archimedes principle-based archimedes optimization algorithm (AOA) on an optimal overcurrent relay coordination problem from the power system protection area. The proposed algorithm is evaluated on two unique case studies, the IEC microgrid benchmark and a combination of overhead/underground cable systems, to verify its efficiency. The uniqueness of these case studies is that in microgrids due to the incorporation of wind turbines bidirectional current flows whereas in overhead/underground system magnitude of current will be different in the various part of the network. Because of this impact of current in both networks, the relay coordination job becomes challenging. The results demonstrate that the proposed AOA technique outperforms other existing algorithms, such as Particle Swarm optimization, Water Cycle algorithm (WCA), Whale optimization algorithm, Crow Search algorithm, and Evaporation Rate WCA, in terms of computational efforts and performance. Furthermore, the proposed algorithm effectively minimizes the total operating time for both networks without violating any constraints. The study confirms that the proposed algorithm provides an efficient solution for the overcurrent relay coordination problem, which can be beneficial for power system protection. The archimedes principle-based archimedes optimization algorithm is demonstrated to be a promising optimization tool that can effectively solve similar optimization problems in the future.

关键词： archimedes optimization algorithm Time multiplier setting Overcurrent relay CTI

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optimization Based Rice Leaf Disease Classification in Federated Learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第29期83卷 72491-72517页

作者： Tripathy, Ramamani Mandala, Jyothi Pappu, Sasibhushana Rao Gopisetty, Guru Kesava Dasu Chitkara Univ Sch Engn Baddi Himachal Prades India Christ Dept Comp Sci & Engn Bangalore India GITAM Dept Comp Sci & Engn Visakhapatnam Campus Visakhapatnam Andhra Pradesh India JNTUK Kakinada KKR & KSR Inst Technol & Sci Dept Informat Technol Guntur India

Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%.

关键词： Federated learning archimedes optimization algorithm Spotted hyena optimization Kalman filter LeNet

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Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization

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ENERGY REPORTS 2021年 7卷 5612-5626页

作者： She, Chen Jia, Rui Hu, Bei-Ning Zheng, Ze-Kun Xu, Yi-Peng Rodriguez, Dragan TianGong Univ Sch Econ & Management Tianjin 300387 Peoples R China Beijing Jiaotong Univ Sch Econ & Management Dept Construct Management Beijing 100044 Peoples R China Hainan Univ Coll Civil Engn & Architecture Haikou 570100 Hainan Peoples R China Hangzhou HFI Hlth Network Technol Co Ltd Hangzhou Zhejiang Peoples R China Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA

The energy consumption in buildings is increasing, then, a new global multiple-objective optimization method for zero-energy buildings with the comprehensive assessment method is used in this study. The applied optimization design methods for renewable energy systems are the multiple-objective optimization by Enhanced archimedes optimization algorithm (EAOA) on the original design for energy costs and energy optimal models. The Hybrid Gray Multiple-Level Comprehensive Assessment Method (HGMLCAM) is utilized to the optimal model including the solar energy use efficacy, energy conservation, economic and social factors. By the selected decision based on the global optimal model, the best solution is presented. For designing the practical building, the defined solution can be useful for the architects and decision-makers. Consequently, a multiple-objective design for a building located in Shanghai, in China, is studied. Based on the results, it can be concluded that the presented method is efficient to make decisions. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Multiple-objective optimization archimedes optimization algorithm Zero-energy building Solar photovoltaic system Energy-efficiency measures Life cycle energy Life cycle cost

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Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models

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ENERGY REPORTS 2021年 7卷 8973-8996页

作者： Zhang, Guodao Band, Shahab S. Jun, Changhyun Bateni, Sayed M. Chuang, Huan-Ming Turabieh, Hamza Mafarja, Majdi Mosavi, Amir Moslehpour, Massoud Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ Rd Touliu Yunlin 64002 Taiwan Chung Ang Univ Dept Civil & Environm Engn Coll Engn Seoul 06974 South Korea Univ Hawaii Manoa Dept Civil & Environm Engn Honolulu HI 96822 USA Univ Hawaii Manoa Water Resources Res Ctr Honolulu HI 96822 USA Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu Yunlin Taiwan Taif Univ Dept Informat Technol Coll Comp & Informat Technol POB 11099 At Taif 21944 Saudi Arabia Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Obuda Univ Inst Software Design & Dev H-1034 Budapest Hungary Asia Univ Dept Business Adm Taichung Taiwan Calif State Univ San Bernardino Dept Management San Bernardino CA 92407 USA

Solar radiation (SR) is considered as a critical factor in determining energy management. In this research, the potential of the Bayesian averaging model (BMA) was investigated for estimating monthly SR. The inputs were monthly average temperature, wind speed, relative humidity, and sunshine duration. The BMA model was employed to estimate SR by extracting information from multiple adaptive neuro-fuzzy systems (ANFIS) and multi-layer perceptron (MLP) models. In this study, archimedes optimization algorithm (AOA), particle swarm optimization (PSO), genetic algorithm (GA), and bat algorithm (BA) were used to tune the parameters of the ANIFS and MLP. In addition, a multitude of error indices such as root mean square error (RMSE), and Nash Sutcliff efficiency (NSE), and several graphical tools were used to investigate the accuracy of the models. The results showed the better performance of the BMA model than other models for estimating solar radiation. For example, BMA with RMSE of 6.78, MAE of 5.25, and NSE of 0.96 had the best accuracy in the training stage of the Tabriz station. On the other hand, in the testing level of Tehran station, BMA (RMSE=7.89 MJ/m(2), MAE=6.89 MJ/m(2), NSE=0.95) gave the best accuracy, and the MLP model (RMSE= 14.12 MJ/m(2), MAE=12.23 MJ/m(2), and NSE=0.77) gave the worst performance, respectively. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Solar radiation Energy management Soft computing models archimedes optimization algorithm

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Developing support vector regression and radial basis function neural networks in optimized holistic frameworks to estimate hardness properties of concrete

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 8349-8364页

作者： Wang, Chuncha Fujian Chuanzheng Commun Coll Fuzhou Peoples R China

The hardness properties of constructional materials should be investigated as important factors in assessing the performance over the operation period. Two tests are performed to determine the stiffness characteristic, including slump and compressive strength (CS). They must be considered to examine efficiency, durability, and resistance to pressure. Due to the structure's susceptibility and usage in dams, bridges, etc., high-performance concrete must have an appropriate set of these tests. There are two soft-based and laboratory methods for performing these tests. The laboratory method is not economical in terms of cost and time, and artificial intelligence (AI) is used to reduce the aforementioned factors. Models and optimizers use software-based methods to help reduce errors and increase model accuracy. So, The main purpose of this research has been introducing novel ways of coupling an ensemble model with optimizers by adjusting some internal parameters. In this article, two models, the Radial Basis Function Neural network and Support Vector Regression were combined and coupled with General Normal Distribution optimization (GNDO) and archimedes optimization algorithm (AOA) into the two frameworks of SVRRBF-AOA and SVRRBF-GNDO. As a result, the hybrid model of SVRRBF-AOA could perform well by obtaining R-2 and RMSE of 0.9915 and 2.71 for the slump and 0.9845 and 3.34 for CS, respectively.

关键词： High-performance concrete slump compressive strength support vector regression Radial basis function generalized normal distribution optimization archimedes optimization algorithm

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Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems

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ALEXANDRIA ENGINEERING JOURNAL 2023年 73卷 543-577页

作者： Hashim, Fatma A. Abu Khurma, Ruba Albashish, Dheeb Amin, Mohamed Hussien, Abdelazim G. Helwan Univ Fac Engn Helwan Egypt Al Ahliyya Univ Fac Informat Technol Comp Sci Dept Amman Jordan Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Commun Te Comp Sci Dept Salt Jordan Menoufia Univ Fac Sci Al Minufiyah Egypt Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan

archimedes optimization algorithm (AOA) is a new physics-based optimizer that sim-ulates archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engi-neering problems to test the AOA-BSA's ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment's results show the effectiveness of the AOA-BSA in optimiz-ing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algo-rithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability.

关键词： archimedes optimization algorithm Bird Swarm algorithm optimization Metaheuristic Engineering problems

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An efficient computational offloading framework using HAA optimization-based deep reinforcement learning in edge-based cloud computing architecture

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KNOWLEDGE AND INFORMATION SYSTEMS 2023年第1期65卷 409-433页

作者： Saranya, G. Sasikala, E. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Data Sci & Business Syst Chennai Tamil Nadu India

Mobile Cloud Computing (MCC) has emerged as a popular model for bringing the benefits of cloud computing to the proximity of mobile devices. MCC's preliminary goal is to improve service availability as well as performance and mobility features. Edge computing, a novel paradigm, provides rich computing capabilities at the edge of pervasive radio access networks close to users. Designing an efficient offloading technique for edge computing is a major research challenge given the constrained resources. Offloading speeds up processing, which has an impact on service quality in heterogeneous devices. Due to the difficulties of the network states' distribution environment, allocating computing resources is a difficult process. In this study, inputs from various sorts of devices such as cars, mobile phones, and heterogeneous building sources are considered. When an accurate energy estimation model is established to compute the energy consumption of the tasks during offloading, an effective task offloading technique can be derived. The model should select whether or not to conduct offloading based on the computed energy cost. This study proposed a Hybrid Arithmetic archimedes optimization algorithm-based Deep Reinforcement Learning model for computation offloading in heterogeneous devices. The effectiveness of the proposed method is evaluated using different state-of-art methods such as First Upload Round and Second Upload Round (FUR-SUR), Efficient Dynamic-Decision Based Task Scheduler, Context-aware computation offloading and Price-based distributed offloading. The proposed method offers superior results to other existing methods. The user's average utility of the proposed method increases by 410% contrasted to FUR-SUR.

关键词： Computation offloading Edge computing Deep reinforcement learning Arithmetic optimization archimedes optimization algorithm

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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