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Routing Protocol for Wireless Sensor Networks Based on archimedes optimization algorithm

IEEE SENSORS JOURNAL 2022年第15期22卷 15561-15573页

作者： Yao, Yindi Xie, Dangyuan Li, Ying Wang, Chen Li, Yangli Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Shaanxi Peoples R China

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime.

关键词： Wireless sensor networks Clustering algorithms Sensors Energy consumption optimization Routing protocols Routing archimedes optimization algorithm network lifetime network energy consumption route optimization wireless sensor networks

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Hybrid archimedes optimization algorithm enhanced with mutualism scheme for global optimization problems

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第7期56卷 6885-6946页

作者： Varol Altay, Elif Manisa Celal Bayar Univ Dept Software Engn Manisa Turkey

archimedes optimization algorithm (AOA) is a recent metaheuristic method inspired by the archimedes principle, which is the law of physics. Like other metaheuristic methods, it suffers from the disadvantages of being stuck in local areas, suffering from weak exploitation abilities, and an inability to maintain a balance between exploration and exploitation. To overcome these weaknesses, a new hybrid Mutualism archimedes optimization algorithm (MAOA) method has been proposed by combining the AOA and the mutation phase in the Symbiosis organism search (SOS) method. SOS algorithm is known for its exploitation ability. With the mutation phase, it has been used to improve local search for swarm agents, help prevent premature convergence and increase population diversity. To verify the applicability and performance of the proposed algorithm, extensive analysis of standard benchmark functions, CEC'17 test suites, and engineering design problems were performed. The proposed method is compared with the recently emerged and popular AOA, SOS, Harris Hawks optimization (HHO), COOT optimization algorithm (COOT), Aquila Optimizer (AO), Salp Swarm algorithm (SSA), and Multi-Verse optimization (MVO) methods, and statistical analyses were performed. The results obtained from the experiments show that the proposed MAOA method has superior global search performance and faster convergence speed compared to AOA, SOS, and other recently emerged and popular metaheuristic methods. Furthermore, this study compares MAOA to five well-established and recent algorithms constructed using various metaheuristic methodologies utilizing nine benchmark datasets to assess the general competence of MAOA in feature selection. Therefore, the proposed method is considered to be a promising optimization method for real-world engineering design problems, global optimization problems, and feature selection.

关键词： archimedes optimization algorithm Symbiosis organisms search Mutualism scheme Metaheuristics Hybrid method Global optimization problems

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Progressive Conditional Generative Adversarial Network Optimized with archimedes optimization algorithm Fostered Inheritance and Innovation of Huizhou Carving Culture

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-11页

作者： Geng, Dianmei Huangshan Univ Sch Arts Huangshan 245041 Peoples R China

The past dynasties literature has very few records as the literature of the earlier ones is a novelty and the hereditary secret historical and cultural assets are now at risk. In this study, the inheritance and innovation of the Huizhou carving culture using a progressive conditional generative adversarial network enhanced using the archimedes optimization algorithm (HCC-PCGAN-AO) is proposed. Initially, the data are gathered using the onsite Huizhou culture website with three-dimensional data scanning technique. The progressive conditional generative adversarial network (PCGAN) is used to design the Huizhou carving. Then, the archimedes optimization algorithm (AOA) is proposed to optimize the Progressive PCGAN classifier, which precisely eliminates errors in the design. It demonstrates the 3D digital carving into architectural cultural property is required and achieves a essential role in preserving the advancing architectural cultural heritage. The proposed HCC-PCGAN-AO method attains 22.13%, 19.46% and 30.65% lower RMSE compared with the existing methods such as Inheritance and Protection of Temple Architectural Cultural Heritage utilizing in the Case of Three-Mountain Kings Ancestral Temple of Jiexi Lintian with Digital Media Technology (IPTA-CH-DMT), Research on Innovative design of tourism cultural and creative products from the perception of Huizhou intangible cultural heritage culture (RID-TCC-HICHC) and Research on the Inheritance along Development of Huizhou Culture in the Construction of New Countryside in Anhui Province (RID-HCC-NCAP) respectively.

关键词： archimedes optimization algorithm Huizhou carving culture Progressive conditional generative adversarial network Hereditary secret historical and cultural assets 3D digital carving

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An efficient photovoltaic modeling using an Adaptive Fractional-order archimedes optimization algorithm: Validation with partial shading conditions

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SOLAR ENERGY 2022年 236卷 26-50页

作者： Yousri, Dalia Shaker, Yomna Mirjalili, Seyedali Allam, Dalia Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Univ Sci & Technol Fujairah USTF Engn Dept Fujirah U Arab Emirates Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

Detecting the maximum power point in the photovoltaic (PV) system under normal and shaded weather conditions with high accuracy is vital to save the harvested power. Providing a robust model that emulates the physical behavior of a combination of particular solar modules is the core of designing a reliable PV system. As the PV models' parameters are not provided in the manufacturing datasheets, there is a persistent need to introduce an efficient and competent tool that provides the optimal parameters of the PV models. Therefore, this paper presents a novel strategy depending on a novel fractional calculus-based optimization technique to detect the optimal parameters of the PV models. The identified parameters globally fulfilled all tested shading conditions of different types and configurations of PV modules, strings, and arrays to verify the optimizer reliability and efficiency. A novel optimization algorithm called an Adaptive Fractional-order archimedes optimization algorithm (A-FAOA) is proposed to identify the single and double diode model parameters for several PV solar cells/modules under various environmental conditions. The proposed algorithm uses a fractional-calculus memory perspective to enhance the exploration stage of the basic archimedes optimization algorithm. In addition, the two-dimensional-Henon map is adopted in the algorithm to tune its parameters adaptively in an attempt to achieve a smooth transition between the exploration and exploitation phases. The developed technique is tested on several experimental datasets for several PV cells/modules under diverse environmental conditions. The proposed algorithm is compared with the recent literature based on statistical analysis and non-parametric tests. Moreover, the fitting curves and the values of error at the maximum power points are provided to demonstrate the superiority of the proposed method. For further evaluation of the reliability of the identified parameters, several PV systems based on

关键词： Double diode PV model Partial shading Single diode PV model PV parameters estimation archimedes optimization algorithm Fractional calculus optimization Artificial Intelligence

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archimedes optimization algorithm based maximum power point tracker for wind energy generation system

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AIN SHAMS ENGINEERING JOURNAL 2022年第2期13卷 101548-101548页

作者： Fathy, Ahmed Alharbi, Abdullah G. Alshammari, Sulaiman Hasanien, Hany M. Jouf Univ Fac Engn Elect Engn Dept Sakakah Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo Egypt

Improving the operation of wind energy generation system is a big challenge especially when it operates under unstable weather conditions. Therefore, installing a tracker for monitoring the maximum power expected to be generated from the wind energy system is essential, this controller is known as maximum power point tracker (MPPT). Hill climbing based approaches were applied for simulating the tracker, however they have limitations in tracking speed and efficiency. This paper proposes a recent efficient approach of archimedes optimization algorithm (AOA) for simulating MPPT installed with the wind energy generation system. The constructed system composes wind turbine (WT) connected to a perma-nent magnet synchronous generator (PMSG), the AC output power from the generator is converted to DC via 3-phase rectifier, the DC voltage is the input of boost converter which is controlled via its MOSFET duty cycle. The designing process is presented as an optimization problem considering the electrical out-put power from the system as the target. The proposed AOA tunes the converter duty cycle to maximize the output power. Three scenarios are followed in this work which are fixed wind speed, variable wind speed, and real wind speed recorded at four sites in Saudi Arabia (Sakaka, Riyadh, Jeddah, and Jizan). The obtained results via the proposed approach are compared with the results obtained via cuckoo search (CS), grasshopper optimization algorithm (GOA), and electric charged particle optimization (ECPO). The results confirmed the robustness of the proposed AOA-MPPT in achieving the best performance of wind energy generation system.(c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： archimedes optimization algorithm Maximum power point tracker optimization Wind energy generation system

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archimedes optimization algorithm based parameter extraction of photovoltaic models on a decent basis for novel accurate RMSE calculation

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Hussain, Md Tahmid Hussan, Md Reyaz Tariq, Mohd Sarwar, Adil Ahmad, Shafiq Poshtan, Majid Mahmoud, Haitham A. Aligarh Muslim Univ Zakir Husain Coll Engn & Technol Dept Elect Engn Aligarh India King Saud Univ Coll Engn Ind Engn Dept Riyadh Saudi Arabia Cal Poly State Univ Dept Elect Engn San Luis Obispo CA USA

Solar photovoltaic (PV) technology stands as a promising alternative to conventional fossil fuel-based power generation, offering pollution-free and low-maintenance energy production. To harness its potential effectively, understanding the power generation process and accurately modeling solar PV systems are essential. Unfortunately, manufacturers often do not provide the necessary parameters for modeling solar cells, making it challenging for researchers. This research employs the archimedes optimization algorithm (AOA), an optimization technique, to determine unknown parameters for the PVM752 GaAs thin film solar cell and the RTC France solar cell. The modeling of these solar cells utilizes both a Single Diode Model (SDM) and a Double Diode Model (DDM). Performance evaluations are conducted using the sum of individual absolute errors (SIAE) and a novel root mean square error (RMSE) method. Comparing the effectiveness of the AOA with other optimization methods, The RMSEs for the AOA applied to the SDM and DDM of RTC France solar cell were 3.7415 x 10-3 and 1.0033 x 10-3. Similarly, for PVM752 GaAs thin film solar cell were 1.6564 x 10-3, and 0.00106365, respectively. The SIAE values for both solar diode models of RTC France cells were 0.071845 and 0.021268, respectively. For the PVM752 GaAs thin film, the corresponding SIAE values were 0.031488 and 0.040224. The results highlight the efficiency of the AOA-based approach, showcasing consistent convergence and a high level of accuracy in obtained solutions. The suggested approach produces superior results with a lower RMSE compared to other algorithms, demonstrating its efficacy in determining solar PV parameters for modeling purposes.

关键词： archimedes optimization algorithm double diode model (DDM) PVM752 GaAs thin film parameter extraction root mean square error (RMSE) RTC France solar cell single diode model (SDM)

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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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Improved archimedes optimization algorithm with Deep Learning Empowered Fall Detection System

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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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Defect of archimedes optimization algorithm and its verification

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SOFT COMPUTING 2023年第2期27卷 701-722页

作者： Ding, Guiyan Wang, Wentao Liu, Hao Tu, Liangping Univ Sci & Technol Liaoning Anshan Peoples R China

archimedes optimization algorithm (AOA) is a new meta-heuristic algorithm which is based on archimedes principle and mimics the buoyancy force received by an object in water. The AOA is designed according to physical principles and has been the object of many scholars' research because of its simple and reliable performance. In the course of the study, this paper finds that the AOA is flawed. In the iterative update of the algorithm, the buoyancy principle applied to the object is not completely followed. Through the investigation and analysis of this problem, it is found that the algorithm design which follows the buoyancy principle completely is more advantageously and persuasively, and named the corrected algorithm CAOA. The performance of the CAOA and other comparison optimization algorithms is tested in benchmark functions CEC2017 under equal conditions to verify the ideas proposed in this paper. In the solution accuracy with dimensions of 30 and 50, the comprehensive score of the CAOA is 31 and 33 and ranks first in all algorithms. In the statistical analysis, the CAOA compared with other algorithms one by one, and achieved the best results in all test functions. When compared with other algorithms, the CAOA ranked first. It is hoped that the verification of the ideas in this paper will help the AOA to develop better and optimize the development of the algorithm.

关键词： Meta-heuristic archimedes optimization algorithm optimization algorithm Defect New verification method

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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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