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An effective design of hybrid renewable energy system using an improved archimedes optimization algorithm: A case study of Farafra, Egypt

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期283卷

作者： Kharrich, Mohammed Selim, Ali Kamel, Salah Kim, Jonghoon Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

The Improved archimedes optimization algorithm (IAOA) is presented and applied to design a hybrid renewable energy system (HRES) for a microgrid system in the Farafra region of Egypt. The studied microgrid consists of three scenarios based on PV panels, wind turbine systems, diesel generators, and a battery energy storage system (BESS). The objective is to minimize the design function of the net present cost (NPC) that englobes all expenses during the project lifetime, respecting three constraints: the renewable fraction index, loss of power supply probability, and availability. The simulation results are compared with several known algorithms, such as the original archimedes optimization algorithm (AOA), artificial electric field algorithm (AEFA), Equilibrium optimizer (EO), Grey Wolf optimizer (GWO), and Harris Hawks optimization (HHO) algorithms. The results prove the ability of the proposed algorithm to solve the problem design, and they also demonstrate its superior efficiency to competing algorithms. The best-found HRES is the PV panel/wind turbine/diesel generator/battery storage system HRES, the NPC is $187,181, equivalent to cost energy (LCOE) of 0.213 $/kWh. The constraints are respected, the reliability is approximately 5%, the renewable fraction index is close to 90%, and the availability is approximately 100%. From the results, it is observed that the synergy of PV and wind systems is mandatory in such areas, and the battery also plays an important role in managing and arranging the energetic flow in HRES systems. The benchmark functions are tested using 23 functions, which proved that the IAOA performed better than the original AOA algorithm.

关键词： Hybrid Renewable Energy Systems Microgrid Sizing Microgrid Design archimedes optimization algorithm Metaheuristic algorithms

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MLBFN optimized with archimedes optimization algorithm for SRCE

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Kannaiah, Sathish Kumar Sonaimuthu, Sridevi Balaji, Navaneetha Rama Krishnan Alangudi Aravinth, Sinnappampatty Seshadri Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai Tamil Nadu India

The Internet of Things (IoT) and its devices have become an integral part of the people 's daily lives recently. The growing demand for intelligent applications indicates that the IoT improves regular automation and intelligent sensing, which improves quality of life. Data present in a variety of forms and formats is the fundamental element of the IoT ecosystem. Then, the gathered information is utilized to generate context awareness and arrive at significant conclusions. Numerous obstacles related to object security are used to maintain on-going services with many benefits using IoT. In this manuscript, Multi-Lead-Branch Fusion Network optimized using archimedes optimization algorithm for Securing Resource Constrained Environments (MLBF-ArOA-SRCE)is proposed. Initially, the data are acquired from the N-BaIoT dataset. The input data are pre-processed using Structural Interval Gradient Filtering (SIGF) which requires using the common organising techniques to put the data in an accessible format, like removing extra spaces and entries without values. Then,the pre-processed data are fed intoHexadecimal Local Adaptive Binary Pattern (HLABP) for extracting features. Then, the extracted features are provided to the Multi-Lead-Branch Fusion Network (MLBFN) which classifies the benign and malicious attack. TheMLBFN does not express any adoption of optimization strategies for scaling the ideal parameters for Securing Resource Constrained Environments. Hence, archimedes optimization algorithm (ArOA) is utilized to improve the MLBFN weight parameters. The performance of the proposed techniqueis examined using performance metrics like precision, recall, f-measure, specificity, and accuracy. The proposed MLBF-ArOA-SRCE method provides 38%, 14%, 29.93% higher recall;26.87%, 25.41%, 17.92 %higher accuracy;30.88%, 13.29%, 25.71% higher specificity compared with existing approaches like RER-EML, FOG-PDM, ALSN-SSP respectively.

关键词： archimedes optimization algorithm Hexadecimal Local Adaptive Binary Pattern Multi-Lead-Branch Fusion Network Resource Constrained Environments Structural interval gradient filtering

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Infill Well Placement optimization for Polymer Flooding in Offshore Oil Reservoirs via an Improved archimedes optimization algorithm with a Halton Sequence

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ENERGIES 2024年第22期17卷 5552页

作者： Tang, Engao Zhang, Jian Xia, Anlong Jin, Yi Li, Lezhong Chen, Jinju Hu, Biqin Sun, Xiaofei State Key Lab Offshore Oil & Gas Exploitat Beijing 100028 Peoples R China CNOOC Res Inst Ltd Beijing 100028 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China

Infill drilling is one of the most effective methods of improving the performance of polymer flooding. The difficulties related to infill drilling are determining the optimal numbers and placements of infill wells. In this study, an improved archimedes optimization algorithm with a Halton sequence (HS-AOA) was proposed to overcome the aforementioned difficulties. First, to optimize infill well placement for polymer flooding, an objective function that considers the economic influence of infill drilling was developed. The novel optimization algorithm (HS-AOA) for infill well placement was subsequently developed by combining the AOA with the Halton sequence. The codes were developed in MATLAB 2023a and connected to a commercial reservoir simulator, Computer Modeling Group (CMG) STARS, Calgary, AB, Canada to carry out infill well placement optimization. Finally, the HS-AOA was compared to the basic AOA to confirm its reliability and then used to optimize the infill well placements for polymer flooding in a typical offshore oil reservoir. The results showed that the introduction of the Halton sequence into the AOA effectively increased the diversity of the initial objects in the AOA and prevented the HS-AOA from becoming trapped in the local optimal solutions. The HS-AOA outperformed the AOA. This approach was effective for optimizing the infill well placement for polymer flooding processes. In addition, infill drilling could effectively and economically improve the polymer flooding performance in offshore oil reservoirs. The net present value (NPV) of the polymer flooding case with infill wells determined by HS-AOA reached USD 3.5 x 108, which was an increase of 7% over that of the polymer flooding case. This study presents an effective method for optimizing infill well placement for polymer flooding processes. It can also serve as a valuable reference for other optimization problems in the petroleum industry, such as joint optimization of well control and placement.

关键词： well placement polymer flooding archimedes optimization algorithm Halton sequence offshore oil reservoirs reservoir simulation

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Doppler shift with archimedes optimization algorithm for localizing unknown nodes in underwater sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第17期36卷 e5604-e5604页

作者： Kaliraj, S. Hariharan, B. Sivakumar, V Josephin, J. S. Femilda Siva, R. Prakash, P. N. Senthil Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 India SRM Inst Sci & Technol Coll Engn & Technol Sch Comp Dept Computat Intelligence Srm Nagar Kattankulathur India Istinye Univ Fac Engn & Nat Sci Comp Engn Istanbul Turkiye Vellore Inst Technol Dept Comp Sci & Engn Chennai India

The issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with archimedes optimization algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node;here archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm optimization (PSO) and Whale optimization algorithm (WOA).

关键词： and localization error archimedes optimization algorithm doppler shift frequency underwater sensor networks

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A Novel MIMO Radar Orthogonal Waveform Design algorithm Based on the Multi-Objective Improved archimedes optimization algorithm

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REMOTE SENSING 2023年第21期15卷 5231-5231页

作者： Wang, Yanjiao Chen, Mingchi Northeast Elect Power Univ Sch Elect Engn 169 Changchun Rd Jilin 132012 Peoples R China

Realization and enhancement of detection techniques for multiple-input-multiple-output (MIMO) radar systems require polyphase code sequences with excellent orthogonality characteristics. Therefore, orthogonal waveform design is the key to realizing MIMO radar. Conventional orthogonal waveform design methods fail to ensure acceptable orthogonal characteristics by individually optimizing the autocorrelation sidelobe peak level and the cross-correlation sidelobe peak level. In this basis, the multi-objective archimedes optimization algorithm (MOIAOA) is proposed for orthogonal waveform optimization while simultaneously minimizing the total autocorrelation sidelobe peak energy and total cross-correlation peak energy. A novel optimal individual selection method is proposed to select those individuals that best match the weight vectors and lead the evolution of these individuals to their respective neighborhoods. Then, new exploration and development phases are introduced to improve the algorithm's ability to increase its convergence speed and accuracy. Subsequently, novel incentive functions are formulated based on distinct evolutionary phases, followed by the introduction of a novel environmental selection method aimed at comprehensively enhancing the algorithm's convergence and distribution. Finally, a weight updating method based on the shape of the frontier surface is proposed to dynamically correct the shape of the overall frontier, further enhancing the overall distribution. The results of experiments on the orthogonal waveform design show that the multi-objective improved archimedes optimization algorithm (MOIAOA) achieves superior orthogonality, yielding lower total autocorrelation sidelobe peak energy and total cross-correlation peak energy than three established methods.

关键词： MIMO radar waveform design archimedes optimization algorithm multi-objective

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Modulation Signal Recognition of Underwater Acoustic Communication Based on archimedes optimization algorithm and Random Forest

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SENSORS 2023年第5期23卷 2764-2764页

作者： Wang, Maofa Zhu, Zhenjing Qian, Gaofeng Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Marine Technol & Equipment Res Ctr Hangzhou 310012 Peoples R China

This paper researches the recognition of modulation signals in underwater acoustic communication, which is the fundamental prerequisite for achieving noncooperative underwater communication. In order to improve the accuracy of signal modulation mode recognition and the recognition effects of traditional signal classifiers, the article proposes a classifier based on the archimedes optimization algorithm (AOA) and Random Forest (RF). Seven different types of signals are selected as recognition targets, and 11 feature parameters are extracted from them. The decision tree and depth obtained by the AOA algorithm are calculated, and the optimized random forest after the AOA algorithm is used as the classifier to achieve the recognition of underwater acoustic communication signal modulation mode. Simulation experiments show that when the signal-to-noise ratio (SNR) is higher than -5dB, the recognition accuracy of the algorithm can reach 95%. The proposed method is compared with other classification and recognition methods, and the results show that the proposed method can ensure high recognition accuracy and stability.

关键词： modulation recognition random forest archimedes optimization algorithm underwater acoustic communication

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Adaptive variational mode decomposition based on archimedes optimization algorithm and its application to bearing fault diagnosis

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MEASUREMENT 2022年 191卷 110798-110798页

作者： Wang, Junxia Zhan, Changshu Li, Sanping Zhao, Qiancheng Liu, Jiuqing Xie, Zhijie Northeast Forestry Univ Sch Traff & Transportat Harbin 150042 Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150042 Peoples R China

Variational mode decomposition (VMD) is widely used in rotating machinery fault diagnosis. However, the choice of its main parameters is often based on experience, affecting the decomposition results. Aiming to mitigate this drawback, an adaptive VMD method using the archimedes optimization algorithm (AOA) is presented. Firstly, the computational domain of the objective function is set to the amplitude spectrum of the signal envelope spectrum. Secondly, a correlation waveform index (Cwi) is proposed to evaluate the complexity of the signal. The minimum average value of the Cwi of all intrinsic modal functions (IMFs) is taken as the objective function. Finally, the AOA is used to search for the optimal mode number and penalty factor to find IMFs which are sensitive to fault features. Compared to the other improved VMD methods, the proposed method has a better performance in extracting the fault characteristics from the simulated and actual cases.

关键词： Variational mode decomposition Fault diagnosis Signal processing archimedes optimization algorithm

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Data-driven continuous-time Hammerstein modeling with missing data using improved archimedes optimization algorithm

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RESULTS IN ENGINEERING 2024年 24卷

作者： Islam, Muhammad Shafiqul Ahmad, Mohd Ashraf Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Pekan 26600 Pahang Malaysia

This research introduces the improved archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. It addresses the limitations of the original archimedes optimization algorithm (AOA) through two key modifications: rebalancing the exploration and exploitation phases and mitigating local optima trapping issues. The primary focus is on developing a novel data-driven approach for modeling continuous-time Hammerstein models, particularly in the presence of missing output data. Four levels of missing measurement data (5 %, 15 %, 35 %, and 50 %) were considered, with data points randomly replaced with zeros. Models were tested with both complete and missing output data to evaluate the robustness of the IAOA-based method. The proposed based method identified linear and nonlinear subsystem variables in a continuous-time Hammerstein model leveraging input and output data, validated through two practical experiments: a Twin Rotor System and an Electromechanical Positioning System. The performance was assessed by examining various factors, including the convergence curve of the fitness function and its statistical analysis, responses in the frequency and time domains, Wilcoxon's rank-sum test, and computational time. Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. The findings showed that the proposed IAOA-based method delivered highly accurate and consistent solutions, proving it to be the most effective and reliable method compared to the others assessed.

关键词： Data-driven Hammerstein modeling Metaheuristics algorithms Data-driven control with missing data archimedes optimization algorithm Twin rotor system Electrical-mechanical positioning system

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Enhanced cervical precancerous lesions detection and classification using archimedes optimization algorithm with transfer learning

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SCIENTIFIC REPORTS 2024年第1期14卷 1-14页

作者： Allogmani, Ayed S. Mohamed, Roushdy M. Al-shibly, Nasser M. Ragab, Mahmoud Univ Jeddah Coll Sci & Arts Khulis Dept Biol Jeddah Saudi Arabia Jerash Univ Coll Appl Hlth Sci Physiotherapy Dept Jerash Jordan King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the archimedes optimization algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

关键词： Cervical cancer Human papillomavirus archimedes optimization algorithm Transfer learning Medical image

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Support Vector Machine Optimized by Henry Gas Solubility optimization algorithm and archimedes optimization algorithm to Solve Data Classification Problems

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ENGINEERING LETTERS 2023年第2期31卷 531页

作者： Yu, Ji-Sheng Zhang, Sheng-Kai Wang, Jie-Sheng Li, Song Sun, Ji Wang, Rui Yingkou Inst Technol Sch Elct Engn Yingkou 115014 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Yingkou Northem Steel Drum Mfg Equipment Technol C Yingkou 115014 Liaoning Peoples R China

Support vector machine (SVM) is the minimization of structural risk to construct a better hyperplane to maximize the distance between the hyperplane and the simple points on both sides of hyperplane. Two improved physics-wise swarm intelligence optimization algorithms (Henryl gas solubility optimization algorithm and archimedes optimisation algorithm) were proposed based on Levy flight operator, Brownian motion operator and Tangent flight motion operator to optimize the penalty factor and kernel function parameters of SVM so as to enhance its global and local search ability. Iinally, the Iris datasets, Strip surface defect datasets, Wine datasets and Wisconsin datasets of breast cancer in UCh datasets were selected to carry out the simulation experiment. Sinulation results show that optimizing SVM based on improved physical-wise swarm intelligence algorithms can effectively improve the classification accuracy.

关键词： SVM data classification hemy gas solubility optimization algorithm archimedes optimization algorithm

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