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Rayleigh surface wave inversion based on an improved archimedes optimization algorithm

APPLIED GEOPHYSICS 2023年 1-13页

作者： Zhen-An, Yao Wang, Ren Yu, Fu Long-Feng, Chen Hong-Xing, Li Xiang-Teng, Wang Wang, Pan East China Univ Technol Jiangxi Engn Res Ctr Earthquake Prevent & Mitigat Nanchang 330013 Jiangxi Peoples R China

Surface wave exploration has the advantages of high shallow resolution, small site limitations, and convenient construction, increasing its use in near-surface exploration. Dispersion curve inversion is an important step in surface wave exploration and is directly related to the acquisition of underground formation information. Similar to numerous geophysical inversion problems, dispersion curve inversion has multiparameter and multiextreme characteristics and is difficult to solve using a linear method. In this paper, a new dispersion curve inversion method based on the archimedes optimization algorithm (AOA), namely the improved AOA (IAOA), is proposed. IAOA optimizes the population initialization based on AOA and adds the capability to automatically balance global search and local development performance in the iteration process, which enriches the population information in the later stage of AOA iteration. The algorithm is used to invert the noise and noise-free base-step scatter curves of the three theoretical models to test the performance of IAOA for dispersion curve inversion. In the inversion test of the theoretical model of noise-free data, the particle swarm optimization (PSO) algorithm and AOA were also tested in the same inversion test to compare the performances of the PSO, AOA, and IAOA algorithms. The results of the model test revealed that IAOA can stably and quickly converge to the optimal solution, and the inversion results have strong credibility, good noise immunity, and can be effectively used for dispersion curve inversion. Finally, the measured data from the Wyoming area of the United States were utilized to test the capability of IAOA to invert actual data. The inversion results indicated that IAOA has strong practicability and can obtain effective formation information.

关键词： Rayleigh wave Dispersion curve inversion Global optimization archimedes optimization algorithm

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Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using archimedes optimization algorithm

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Clean Energy 2022年第2期6卷 271-287页

作者： Varaprasad Janamala K Radha Rani Department of Electrical and Electronics Engineering School of Engineering and TechnologyCHRIST(Deemed to be University)Bangalore-560074KarnatakaIndia Department of Electrical&Electronics Engineering R.V.R.&J.C.College of EngineeringGuntur-522019Andhra PradeshIndia

This paper proposes to resolve optimal solar photovoltaic(SPV)system locations and sizes in electrical distribution networks using a novel archimedes optimization algorithm(AOA)inspired by physical principles in order to minimize network dependence and greenhouse gas(GHG)emissions to the greatest extent *** sensitivity factors are used to predefine the search space for sites,and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power *** with composite agriculture loads on a practical Indian 22-bus agricultural feeder,a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy ***,the voltage profile of the network has been enhanced,resulting in significant reductions in distribution *** results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature,and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex,non-linear and multivariable optimization problems.

关键词： solar photovoltaic system electrical distribution network composite load modelling archimedes optimization algorithm distributed generation

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Power System Stability Enhancement Based Optimized PID controller using archimedes optimization algorithm

Power System Stability Enhancement Based Optimized PID contr...

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IEEE Conference on Power Electronics and Renewable Energy (IEEE CPERE)

作者： Osheba, Dina S. Hemeida, Mahmoud Gamal Senjyu, Tomonobu Hussein, Mahmoud M. Menoufia Univ Fac Engn Dept Elect Engn Shibin Al Kawm 32511 Egypt Minia Higher Inst Engn Dept Elect Engn Al Minya Egypt Univ Ryukyus Fac Engn Dept Elect & Elect Engn Nishihara 9030213 Japan Aswan Univ Fac Energy Engn Elect Engn Dept Aswan 81528 Egypt

ISBN: (纸本)9781665452335

power system always suffering from sudden changes which affect power system stability. In this study, a single machine infinite bus (SMIB) power system based nonlinear model has been studied with and without optimized power system stabilized (PSS). A new metaheuristic algorithm inspired from physics lows named archimedes optimization algorithm (AOA) has been used to tune parameters of PSS based proportional integral derivative (PSS-PID) controller. Two types of disturbances have been simulated to measure the efficiency of the proposed techniques, electrical three phase short circuit fault at generator terminals and a mechanical disturbance on the rotor shaft. The SMIB based optimized PSS-PID provides a stable system with lower oscillation and fast response. AOA greatly improve the characteristics of the system under study compared to manually tune optimized PSS-PID controller.

关键词： Power system stability PID controller SMIB non-linear model archimedes optimization algorithm

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Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)^µ – Iλ-based archimedes optimization algorithm

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Marhoon, Hamzah M. Basil, Noorulden Mohammed, Abdullah Fadhil Systems Engineering Department College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq

This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the archimedes optimization algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportional-integral-derivative (FOPID) controller gains. To overcome this, the study introduces a three-phase approach: MDNR-based NARMA-L2 controller Pre-process and Identification, Enhancement of NARMA-L2 controller-based NARMA(L2)-FOP+D(ANFIS)^μ-I^λ, and Evaluation of FOPID criteria-based AOA. This approach integrates NARMA-L2 for criterion weighting and ANFIS for AOA selection, validated through NARMA(L2)-FOP+D(ANFIS)^μ-I^λ evaluation, showcasing the efficacy of the proposed methodology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： ANFIS archimedes optimization algorithm Fractional order proportional integral derivative controller Medical Defense Nanorobots NARMA-L2 controller

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A novel chaotic archimedes optimization algorithm and its application for efficient selection of regression test cases

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International Journal of Information Technology (Singapore) 2023年第2期15卷 1055-1068页

作者： Verma, Abhishek Singh Choudhary, Ankur Tiwari, Shailesh Dr. A.P.J. Abdul Kalam Technical University Lucknow India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India Krishna Engineering College Ghaziabad India

Nature is an ocean of knowledge, which inspires living beings to solve their complex problems. Researchers also utilized this knowledge to solve complex engineering problems. archimedes optimization algorithm (AOA) is the latest nature-inspired metaheuristic algorithm that follows the famous principle of physics. But similar to other metaheuristics this also suffers from premature convergence. In this paper, the authors have proposed a novel chaotic variant of the archimedes optimization algorithm named as Chaotic archimedes optimization algorithm (CAOA) to improve the performance of AOA. The proposed CAOA has been further utilized to solve Regression test case selection problem, which is a well-known optimization problem. The performance of the proposed CAOA approach is first compared with ten chaotic variants of AOA and original AOA. The results of statistical tests show that the chaotic maps (mainly Singer Map) are able to improve the fault coverage capabilities of AOA. Further CAOA with Singer Map is compared against state-of-the-art approaches such as AOA, Arithmetic optimization algorithm, and Bat algorithm and proved its superiority. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： archimedes optimization algorithm CAOA Chaotic map Metaheuristic Regression test case selection Regression testing SIR

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Remote Sensing Image Watermarking using archimedes optimization algorithm and Discrete Cosine Transform

Remote Sensing Image Watermarking using Archimedes Optimizat...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Bodke, Minal Chaudhari, Sangita DY Patil Deemed Univ Ramrao Adik Inst Technol Nerul India Pimpri Chinchwad Coll Engn & Res Ravet India

ISBN: (纸本)9798350360332;9798350360325

The unprecedented growth in internet users and various social media platforms increases storage and transfer of digital data and hence leads towards security of this data. The data security ensures the ownership of the multimedia content. Watermarking, steganography, and encryption are the key technologies used to preserve multimedia content ownership. The multispectral images consist of many spectral bands of the images where watermarking is challenging to apply. This paper presents the multispectral image watermarking using the archimedes optimization algorithm (AoA) and Discrete Cosine Transform (DCT). The AoA algorithm selects the spectral band with the highest variability as a cover image for watermarking. The DCT is used to watermark the secret logo in the cover image. The performance of the suggested watermarking scheme is estimated based on peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index (SSIM).

关键词： archimedes optimization algorithm Discrete cosine transform watermarking Data security Image Watermarking

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Secure authentication scheme with archimedes optimization algorithm for load balancing technique in fog computing

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3861-3869页

作者： Premkumar, N. Santhosh, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

In this paper, to develop an efficient secure authentication scheme and load balancing technique in fog computing. To achieve an efficient secure authentical scheme in addition load balancing method in fog computing, Hybrid Edge DataCenters (HEDC) is developed in this paper. Normally, the EDC can be utilized to set up as a distributed system in addition it is located among the data source and cloud datacentre. This EDC network is also placed in the intermediate layer in the fog hierarchy among cloud datacentres in addition Internet of Things (IoT). This EDC is operated as an intermediate layer in fog computing which is responsible for load balancers and secure authentication in distributing the workload. This EDC fails to achieve efficient secure authentication and manage the workload in fog computing. So, the proposed HEDC is utilized to enable efficient authentication and workload management (load balancing). The proposed method is a combination of EDC and archimedes optimization algorithm (AOA). Based on resource utilization and response time, efficient load balancing is achieved. Additionally, the secure authentication scheme is also attained with the assistance of the HEDC. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： archimedes optimization algorithm Data access control EDC Fog computing Internet of things Security enhancement

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Power System Harmonics Estimation using Hybrid archimedes optimization algorithm-based Least Square Method 13

Power System Harmonics Estimation using Hybrid Archimedes Op...

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13th International Conference on Information and Communication Technology and System (ICTS)

作者： Apon, Hasan Jamil Abid, Md Shadman Morshed, Khandaker Adil Nishat, Mirza Muntasir Faisal, Fahim Moubarak, Nchouwat Ndumgouo Ibrahim Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781665440592

Estimating harmonics of a power system with different optimization techniques has emerged as a potential field of research in recent times. The amount of necessary information in an unknown signal, polluted with noise can be effectively determined by utilizing stochastic optimization techniques. In this context, this study proposes a hybridized algorithm termed as archimedes optimization algorithm-based least square (AOA-LS) technique for estimation of harmonics of a power system. The proposed optimization algorithm contributes in predicting the phases of the harmonic signal and conventional least-square (LS) method determines the amplitudes. The simulation was carried out for a voltage wave obtained from a standard testing module's load bus terminal under two noisy conditions : Uniform noise and Gaussian noise. Furthermore, for each noisy situation, the signal-to-noise ratios (SNR) are set to 0 dB, 10 dB, 20 dB, and 40 dB, respectively. For the purpose of comparative analysis, performance of the proposed AOA-LS scheme is evaluated and compared with three of the other techniques known as Firefly algorithm-based LS (FA-LS), Particle swarm optimization with passive congregation based LS (PSOPC-LS), and Artificial bee colony based LS (ABC-LS). According to the findings, the proposed algorithm surpasses all the algorithms in terms of estimation accuracy and computational time.

关键词： archimedes optimization algorithm Least square Power system harmonics Harmonics estimation Power quality

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Binary archimedes optimization algorithm for Computing Dominant Metric Dimension Problem

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Intelligent Automation & Soft Computing 2023年第10期38卷 19-34页

作者： Basma Mohamed Linda Mohaisen Mohammed Amin Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityShebin Elkom32511Egypt Faculty of Computer and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in *** dominant metric dimension of G is the cardinality number of the minimum dominant resolving *** dominant metric dimension is computed by a binary version of the archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving *** feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving *** is the first attempt to determine the dominant metric dimension problem *** proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)*** results confirm the superiority of the BAOA for computing the dominant metric dimension.

关键词： Dominant metric dimension archimedes optimization algorithm binary optimization alternate snake graphs

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Recent approach based heterogeneous comprehensive learning archimedes optimization algorithm for identifying the optimal parameters of different fuel cells

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ENERGY 2022年第0期248卷 123587-123587页

作者： Fathy, Ahmed Babu, Thanikanti Sudhakar Abdelkareem, Mohammad Ali Rezk, Hegazy Yousri, Dalia Jouf Univ Fac Engn Elect Engn Dept Al Jouf Saudi Arabia Chaitanya Bharathi Inst Technol CBIT Dept Elect & Elect Engn Hyderabad 500075 India Univ Sharjah Dept Sustainable & Renewable Energy Engn POB 27272 Sharjah U Arab Emirates Minia Univ Chem Engn Dept Al Minya Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt

A consistent and precise mathematical modeling play a vital role in the performance analysis of fuel cells (FCs) system. Model's efficiency completely depends on design accuracy. Thereby the modeling and estimation of FCs' parameters attracted numerous researchers. In this article, new innovative algorithms named heterogeneous comprehensive learning archimedes optimization algorithm (HCLAOA) for effective modeling of proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) is proposed. To assess the performance of the proposed algorithm, two ratings of PEMFC stacks such as PEMFC 250 W and 500 W (NedStack PS6, BCS 500W, and SR-12PEM 500W) are considered and evaluated under different levels of pressures and temperatures. Further, in case of SOFC, steady-state and dynamic state models are considered. The steady-state SOFC model is investigated with four different levels of temperatures, and the dynamic SOFC model is evaluated with the subject of change in demand power. To verify the consistency and effectiveness of HCLAOA algorithm, extensive statistical analysis and various evaluation criteria are thoroughly performed and are successfully compared with the state of the art algorithms like Harris hawks optimizer, Atom search optimizer, Salp swarm optimization algorithm. In addition, a non-parametric test for all considered cases is performed. From the carried-out analysis, the obtained results, and the observations, it is derived that the proposed HCLAOA approach is the most suitable for modeling both PEMFC and SOFC.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： archimedes optimization algorithm Comprehensive learning Parameters estimation Solid oxide fuel cell Proton exchange membrane fuel cell

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