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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Sayyouri, Adil Bencherqui, Ahmed Sayyouri, Mhamed Hafid, Abdelatif Bourkan, Abderrahim Cherkaoui, Abdeljabbar Abdelmalek Essaadi Univ Natl Sch Appl Sci Lab Innovat Technol LIT Tangier Morocco Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Higher Sch Engn Appl Sci ESISA Analyt Lab Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

In this paper, we introduce a novel approach to solving the problem of choosing the parameters of Meixner moments, recognized as robust descriptors for signal and image analysis. Our method is based on the application of the arithmetic optimization algorithm (AOA) to refine the local parameter values of the Meixner polynomials for optimal performance when calculating the corresponding moments. Our algorithm is evaluated on a set of signals of significant dimensions based on quantitative criteria such as mean square error and maximum signal-to-noise ratio. Our simulation results highlight the efficiency of the proposed algorithm, both in signal analysis and in the analysis of two- and three-dimensional images, compared to conventional methods. Therefore, this approach shows great potential in enhancing the precision and efficiency of extracting information from intricate data, thereby introducing fresh opportunities in the realm of signal and image analysis.

关键词： Meixner polynomials Meixner moments Signal reconstruction Signal compression arithmetic optimization algorithm

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Efficient latency-and-energy-aware IoT-fog-cloud task orchestration: novel algorithmic approach with enhanced arithmetic optimization and pattern search

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3311-3324页

作者： Srichandan, Suresh Kumar Majhi, Santosh Kumar Jena, Sudarson Mishra, Kaushik Rao, D. Chandrasekhar Sambalpur University Institute of Information Technology Sambalpur University Jyotivihar Odisha Burla 768019 India Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya Koni C.G. Bilaspur 495009 India Gandhi Institute of Technology and Management Andhra Pradesh Visakhapatnam 530045 India

Nowadays, the load on datacenters has become more and more due to the unprecedented growth of diversified data from many IoT devices;hence, resource utilization has become more difficult. So, Cloud computing emerged as an appealing solution to leverage IoT devices' computational, memory, capacity and resource constraints. However, the physical gap between IoT devices and the Cloud datacentre resulted in latency overhead and incurred high energy consumption due to the centralized nature of datacenter. Thus, Fog computing is introduced as a complementary to Cloud computing to minimize the physical gap and thus, latency by bringing the computations closer to the end devices. However, the placement of tasks to a particular Fog node (FN)/cloud virtual machine (VM) is an NP-hard problem with much higher computational time. Thus, metaheuristic algorithms are a way out as they have a polynomial time complexity. The arithmetic optimization algorithm (AOA) along with the Pattern search algorithm is one of the optimization algorithms, implemented in order to minimise the makespan and maximise resource utilization. This paper proposes an improved AOA when combined with a pattern search algorithm helps in migrating the tasks from an overloaded FN to an underloaded FN more effectively. Finally, the proposed algorithm is compared with similar optimization algorithms on various parameters. From the simulations, it is evident that the proposed AOA-PS-based task scheduling approach outperformed others with a percentage of improvement of 24.2%, 18.6%, 16%, 5.4%, and 23.4% for makespan, resource utilization, response time, energy consumption, and degree of imbalance, respectively. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： arithmetic optimization algorithm Cloud computing Fog computing IoT Pattern search algorithm Resource management Task orchestration

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A novel AOA-based MPPT technique for 2S2P configuration of a solar PV system considering complex irradiations

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ELECTRICAL ENGINEERING 2025年 1-14页

作者： Pardhu, B. S. S. Ganesh Kota, Venkata Reddy Aditya Univ Dept Elect & Elect Engn JNTUK Surampalem 533437 Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Dept Elect & Elect Engn Kakinada 533003 Andhra Pradesh India

Harvesting the optimal power of solar photovoltaic (PV) cell under partial-shaded conditions (PSC) and dynamic irradiation conditions plays a vital role in efficiently utilizing solar power. This paper introduces new metaheuristic method named as arithmetic optimization algorithm (AOA) and is applied to the solar PV cell for searching the global maximum power point (GMPP) at variable irradiance. The suggested approach is modelled, and numerical simulations are performed on PV arrangements with various shading patterns using a MATLAB/Simulink environment. The proposed algorithm minimizes the oscillations in power during variable irradiation. It accurately tracks the GMPP at high tracking speed and efficiency for all shading patterns. The simulation results are compared with the P&O, musical chair algorithm (MCA), and squirrel search algorithm (SSA) in terms of tracking time, efficiency, and maximum power extracted from the panel. The simulation results strongly suggest that the AOA has the highest tracking efficiency of > 99.4% and the convergence time is < 0.0018 s under PSC. Statistical analyses are performed to observe the performance of AOA with P&O, MCA, and SSA. The results show superiority of the proposed algorithm in terms of root mean square error, and relative error of the methods during various shading patterns. Further, an experimental setup was developed, and investigations are carried with uniform radiations. Experimental results suggest that AOA enhanced system parameters over MCA technique.

关键词： Double diode model arithmetic optimization algorithm GMPP PSC Statistical analysis

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Performance evaluation of submerged FSW for aluminum 6061 employing AOA-ANN based on experimental data

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2025年

作者： Shrivastava, Ekta Shrivastava, Pankaj Kumar Singh, Pushpendra Kashyap, Akash Arya, Liladhar AKS Univ Dept Engn Math Satna MP India Govt Engn Coll Dept Mech Engn Ara Bihar India AKTU Lucknow Rajkiya Engn Coll Banda Dept Elect Engn Lucknow UP India Shri G S Inst Technol & Sci Dept Elect Engn Indore MP India

Submerged friction stir welding (SFSW) under water is a relatively new solid state joining process, which combines heating and mechanical work for deformation to achieve high quality, defect-free joints. In the present research aluminum-6061 alloy has been welded by using SFSW. Tool rotational speed, feed and water temperature has been taken as important control variables to estimate the joint performance in terms of hardness and tensile strength. It was observed that the average grain size obtained in stir zone is around 3.5 mu m, the maximum hardness obtained is 87 HV and maximum tensile strength is 175 MPa. Predictive models using artificial neural networks (ANNs) were developed for both the hardness and tensile strength, followed by process optimization utilizing four distinct evolutionary optimization techniques: arithmetic optimization algorithm (AOA), Jaya algorithm and Rao-3 algorithm. Among these, the AOA demonstrated superior performance within the present manufacturing environment. As compared to the maximum experimental values of hardness and tensile strength, the optimization by using AOA, show the improvement of 6.33% and 0.35% in hardness and tensile strength, respectively.

关键词： Submerged friction stir welding artificial neural network modeling optimization arithmetic optimization algorithm

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Hybrid Markov chain-based dynamic scheduling to improve load balancing performance in fog-cloud environment

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2025年 45卷

作者： Khaledian, Navid Razzaghzadeh, Shiva Haghbayan, Zeynab Volp, Marcus Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Esch Sur Alzette Luxembourg Islamic Azad Univ Dept Comp Engn Ardabil Branch Ardebil Iran

Fog computing is a distributed computing paradigm that has become essential for driving Internet of Things (IoT) applications due to its ability to meet the low latency requirements of increasing IoT applications. However, fog servers can become overburdened as many IoT applications need to run on these resources, potentially leading to decreased responsiveness. Additionally, the need to handle real-world challenges such as load instability, makespan, and underutilization of virtual machine (VM) devices has driven an exponential increase in demand for effective task scheduling in IoT-based fog and cloud computing environments. Therefore, scheduling IoT applications in heterogeneous fog computing systems effectively and flexibly is crucial. The limited processing resources of fog servers make the application of ideal but computationally costly procedures more challenging. To address these difficulties, we propose using an arithmetic optimization algorithm (AOA) for task scheduling and a Markov chain to forecast the load of VMs as fog and cloud layer resources. This approach aims to establish an environmentally load-balanced framework that reduces energy usage and delay. The simulation results indicate that the proposed method can improve the average makespan, delay, and Performance Improvement Rate (PIR) by 8.29%, 11.72%, and 4.66%, respectively, compared to the crow, firefly, and grey wolf algorithms (GWA).

关键词： Task scheduling Load balancing arithmetic optimization algorithm Markov chain Fog computing

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Frequency regulation by optimized fuzzy based self-adaptive virtual inertia control for microgrid with variable renewable penetration

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JOURNAL OF ENERGY STORAGE 2024年 104卷

作者： Mohanty, Debidasi Giri, Ankita Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

The increasing use of renewable energy sources are solving environmental issues, energy shortage problems and result in economic growth. Microgrid can provide a framework for connecting RESs to the utility grid, but high penetration of RESs make it difficult to lower the Rate of Change of Frequency (RoCoF). In this research, a selfadaptive Virtual Inertia Control (VIC) technique is proposed to tackle this instability issue. VIC method simulates virtual inertia, enhancing inertia of the overall system and frequency stability. VIC technique links energy storage systems to the utility and helps with power supply. However, an accurate analysis is necessary to regulate the Virtual Inertia (VI) constant value, which could adequately eradicate the adjustment mistakes & system instability. Fuzzy logic controllers can tackle non-linear problems and provide robustness, and reliability. This research presents a fuzzy based self-adaptive VIC system for stable load frequency regulation in low-inertia microgrid. In addition to this, a fuzzy based secondary frequency control is also proposed in this study. A fuzzy based self-adaptive VIC is developed for VI constant emulation using real power fluctuations of RESs and frequency regulations. The arithmetic optimization algorithm technique is used to obtain the optimized controller parameters. Efficacy of the proposed self-adaptive VIC with fuzzy based secondary thermal controller is validated against no VIC with integral and fuzzy based secondary frequency controller, standard VIC with integral as well as fuzzy based secondary frequency controller and self-adaptive VIC with integral secondary controller for high/low renewable sources penetrations and system parameter variations. Finally, real time validation of the proposed VIC approach has conducted with the help of OPAL-RT 5700 and the experimental results are compared with conventional VIC method.

关键词： Load frequency regulation Self-adaptive virtual inertia control Energy storage system Fuzzy logic controller arithmetic optimization algorithm

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A Hybrid Meta-heuristic algorithm for Optimum Micro-robotic Position Control with PID Controller

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-27页

作者： Baihan, Abdullah Ghith, Ehab Garg, Harish Mirjalili, Seyedali Izci, Davut Rashdan, Mostafa Salman, Mohammad Saleem, Kashif King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Ain Shams Univ Fac Engn Dept Mechatron Cairo 11566 Egypt Thapar Inst Engn & Technol Dept Math Patiala 147004 Punjab India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Bursa Uludag Univ Dept Elect & Elect Engn TR-16059 Bursa Turkiye Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11362 Saudi Arabia Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Jadara Univ Res Ctr Irbid Jordan

The present paper aims to propose a novel hybrid algorithm, where the arithmetic optimization algorithm (AOA) and Rat Swarm optimization (RSO) are employed for the proportional-integral-derivative (PID) controller to control the position of a micro-robotics system. In the algorithm proposed, we combine the exploratory mechanisms of AOA with RSO's exploitative behaviors. The proposed algorithm is employed for identifying the PID controller optimal parameters considering six different objective functions. Using CEC 2017 benchmark functions, the proposed hybrid is evaluated, and these functions' performance is compared with the existing multiple algorithms. The statistical results are compared with the AOA, Jellyfish Search optimization, and Harries Hawk optimization algorithm for identifying the optimal PID controller settings considering multiple fitness functions. We consider performance indicators like PID controller parameters, rise time, settling time, and fitness values. The fetched simulation results revealed that, among all investigated fitness functions, the developed controller based on HAOARSO is the most effective algorithm for delivering global optimal solutions with less settling time and rise time, enabling the implementation on such optimization issues. Finally, the validation via MATLAB/Simulink simulations underscores the efficacy of the proposed algorithm.

关键词： PID controller Hybrid algorithm arithmetic optimization algorithm Rat swarm optimization Minimally invasive surgery

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Prediction of variables involved in TEG Dehydration using hybrid models based on boosting algorithms

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COMPUTERS & CHEMICAL ENGINEERING 2024年 188卷

作者： Wang, Fangxiu Zhao, Jiemei Van Hoang, Vo Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430023 Hubei Peoples R China Bialystok Tech Univ Fac Elect Engn Wiejska 45C PL-15531 Bialystok Poland

The extraction of gas from fields often involves impurities, necessitating natural gas processing to separate these impurities. This process typically entails the removal of acid gases (such as carbon dioxide and hydrogen sulfide) and dehydration, commonly achieved through absorption using triethylene glycol (TEG). Efforts to minimize BTEX emissions and maintain optimal dry gas water content are pivotal for enhancing the economic and environmental sustainability of natural gas processing. In this study, the accurate prediction of BTEX and dry gas water contents is aimed by boosting-based methods and hybridization techniques. Four hybrid models, including XGBoost, CatBoost, HGBR, and LightGBoost, were developed in conjunction with the arithmetic optimization algorithm (AOA) meta-heuristic algorithm to optimize and fine-tune hyperparameters. Through a comprehensive case study and comparison of various evaluation indices, the XGBoost-AOA hybrid model emerged as the most accurate in predicting both outputs. Hence, we recommend the XGBoost algorithm optimized with the AOA algorithm as the preferred approach for predicting BTEX and dry gas water contents in natural gas processing.

关键词： Tri ethylene glycol Hybrid Boosting XGBoost arithmetic optimization algorithm Dehydration

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Estimating pile setup parameter using XGBoost-based optimized models

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GEOMECHANICS AND ENGINEERING 2024年第3期36卷 259-276页

作者： Du, Xigang Ma, Ximeng Dong, Chenxi Nikkhoo, Mehrdad Sattari Hebei Univ Water Resources & Elect Engn Dept Transportat Engn Cangzhou 061001 Hebei Peoples R China Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q(t)), average wall friction (f(s)), and effective vertical stress (sigma(vo)), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S-u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm optimization (PSO), Social Spider optimization (SSO), arithmetic optimization algorithm (AOA), and Sine Cosine optimization algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the arithmetic optimization algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the

关键词： arithmetic optimization algorithm cone penetration test extreme gradient boosting model particle swarm optimization pile set-up parameter A social spider optimization

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Sensitivity analysis and comparative assessment of novel hybridized boosting method for forecasting the power consumption

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期249卷

作者： Zhou, Jing Wang, Qingdong Khajavi, Hamed Rastgoo, Amir Huanggang Normal Univ Coll Comp Huanggang 438000 Hu Bei Peoples R China KN Toosi Univ Technol Dept Civil Engn Tehran Iran

This research focuses on the crucial task of accurately forecasting electricity consumption, a key concern in modern societies where electricity is essential for industries, healthcare, and transportation. The study explores the complex factors influencing electricity usage, aiming to improve the accuracy of consumption forecasts. Recognizing the limitations of current forecasting techniques, which often fail to account for the detailed interplay of variables affecting consumption, it introduces innovative methods to address these challenges. By employing sensitivity analysis and advanced hybrid boosting algorithms, such as LightGBM and CatBoost optimized with arithmetic optimization and Fruit Fly optimization algorithms, the research analyzes time-series data on electricity use, weather conditions, and usage timing across three power distribution networks. The sensitivity analysis reveals the significant impact of customer behavior and temperature on consumption patterns. The predictive models, especially the optimized LightGBM, demonstrate remarkable accuracy, with rates of 0.9996 and 0.9968 for training and testing datasets, respectively. These results highlight the effectiveness of the proposed methodologies in providing accurate and reliable forecasts of electricity demand, offering valuable insights for the energy sector to manage resources more efficiently and meet consumption needs effectively.

关键词： Forecasting power consumption Time -series prediction of electricity consumption Sensitivity analysis LightGBM arithmetic optimization algorithm

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