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Improved archimedes optimization algorithm with Deep Learning Empowered Fall Detection System

Computers, Materials & Continua 2022年第8期72卷 2713-2727页

作者： Ala Saleh Alluhaidan Masoud Alajmi Fahd N.Al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human ***,elderly people suffer from several diseases,and fall action is a common situation which can occur at any *** this view,this paper presents an Improved archimedes optimization algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall *** proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image ***,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature *** addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet ***,radial basis function(RBF)network is applied for determining the proper class labels of the test *** showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

关键词： Fall detection intelligent model deep learning archimedes optimization algorithm capsule network

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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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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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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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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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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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EAOA: An Enhanced archimedes optimization algorithm for Feature Selection in Classification

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IEEE ACCESS 2021年 9卷 120795-120814页

作者： Desuky, Abeer S. Hussain, Sadiq Kausar, Samina Islam, Md Akhtarul El Bakrawy, Lamiaa M. Al Azhar Univ Fac Sci Cairo 11754 Egypt Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India Univ Kotli Azad Jammu & Kashmir Dept Comp Sci & IT Kotli Azad Kashmir 11100 Pakistan Khulna Univ Stat Discipline Sci Engn & Technol Sch Khulna 9208 Bangladesh

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is archimedes optimization algorithm (AOA). This paper proposes an Enhanced archimedes optimization algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

关键词： archimedes optimization algorithm classification feature selection machine learning metaheuristic algorithms optimization algorithms

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Comparision of the political optimization algorithm, the archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry

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MATERIALS TESTING 2021年第4期63卷 356-359页

作者： Yildiz, Betul Sultan Pholdee, Nantiwat Bureerat, Sujin Erdas, Mehmet Umut Yildiz, Ali Riza Sait, Sadiq M. Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia

This article focuses on minimizing product costs by using the newly developed political optimization algorithm (POA), the archimedes 'optimization algorithm (AOA), and the Levy flight algorithm (LFA) in product development processes. Three structural optimization methods, size optimization, shape optimization, and topology optimization, are extensively applied to create inexpensive structures and render designs efficient. Using size, shape, and topology optimization in an integrated way, It is possible to obtain the most efficient structures in industry. The political optimization algorithm (POA) is a metaheuristic algorithm that can be used to solve many optimization problems. This study investigates the search capability and computational efficiency of POA for optimizing vehicle structures. By examining the results obtained, we prove the apparent superiority of the POA to other recent famous metaheuristics such as the archimedes optimization algorithm and the Levy flight algorithm. The most important result of this paperwill be to provide an impressive aid for industrial companies to fill the gaps in their product design stages.

关键词： Political optimization algorithm archimedes optimization algorithm Levy flight algorithm Optimum design Vehicle design Shape optimization

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A novel distributed generation planning algorithm via graphically-based network reconfiguration and soft open points placement using archimedes optimization algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1923-1941页

作者： Ali, Ziad M. Diaaeldin, Ibrahim Mohamed El-Rafei, Ahmed Hasanien, Hany M. Aleem, Shady H. E. Abdel Abdelaziz, Almoataz Y. Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Elect Engn Dept Al Kharj 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Tingar 81542 Egypt Ain Shams Univ Fac Engn Engn Phys & Math Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt ETA Elect Co Power Qual Solut Dept 410 Al Haram St Giza 12111 Egypt Arab Acad Sci Technol & Maritime Transport Coll Engn & Technol Elect Energy Dept Giza 12577 Egypt

Hosting capacity (HC) is the mathematical interpretation for the maximum aggregated distributed generations (DGs) penetration among the distribution networks. DGs integration has been adopted as a cleaner energy resource, but it has major operational issues like overvoltage, harmonics, and others. In this work, a novel mathematical optimization algorithm is adopted for HC maximization via sequential network reconfiguration followed by soft open points (SOPs) placement. Besides, a novel SOPs allocation index is proposed to reduce the computational burden taken by the optimization techniques while controlling the discrete allocation variables. Thus, to ensure maximum penetration of DG units and optimum operation scheme, HC maximization is formulated as multi-objective optimization approach, while considering system performance indices as a portion of the objective function. The proposed optimization algorithm succeeded in maximizing the HC of the IEEE 33- node distribution network, the IEEE 69- node network, the 59-node Egyptian distribution network and the 135-node Brazilian distribution network efficiently while improving system performance indices. Further analysis is conducted on the studied distribution systems to assess the effect of load growth on system's HC after allocating both SOPs and DGs. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Graph-theoretic network reconfiguration Multi-objective optimization archimedes optimization algorithm Graph theory Soft open points Real distribution networks Distributed generation Hosting capacity Load growth

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