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Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network

PERVASIVE AND MOBILE COMPUTING 2024年 103卷

作者： Chaudhary, Simran Kapadia, Fatema Singh, Avinesh Kumari, Nidhi Jana, Prasanta K. Indian Inst Technol ISM Dhanbad Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and gravitational search algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%-30% and around 60%-80% lesser number of tasks unassigned in each round as compared to two base algorithms.

关键词： Task offloading Clustering Deadline Prioritization gravitational search algorithm

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Research on Specific Harmonic Elimination Technology Based on Improved Gravity search algorithm 19

Research on Specific Harmonic Elimination Technology Based o...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhang, Wenyi Fei, Xueyu Wang, Bolun Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

With the continuous improvement of industrial automation, the frequency converter has also been widely used. Its core link is the inverter, but in this process, with the generation of a large number of harmonics, it has a bad impact on the output of the inverter, and it is difficult to achieve stable operation. Specific harmonic elimination pulse width modulation (SHEPWM) has unique advantages in improving the output waveform of inverter. However, the problem of solving its switching angle is complex. Traditional numerical algorithms need to provide specific initial values, and the existing intelligent algorithms such as immune algorithm and particle swarm optimization algorithm are easy to fall into the local optimal solution. Therefore, the purpose of the study is to improve the solution accuracy. In this paper, the improved gravity search algorithm is introduced into the solution of nonlinear transcendental harmonic elimination equations, and the program for solving the switching angle is written in the M file of MATLAB. The solved switching angle is used for the simulation study of the inverter to analyze the effect of SHEPWM technology on harmonic elimination. In conclusion, this objective of improving the performance of SHEPWM technology is achieved. The method can be applied to SHEPWM control technology to reduce harmonic content.

关键词： SHEPWM Inverter gravitational search algorithm Nonlinear equations

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Parameters estimation of double-cage induction motors using a hybrid metaheuristic algorithm 21

Parameters estimation of double-cage induction motors using ...

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21st International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Vukasinovic, Jovan Milovanovic, Milos Arsic, Nebojsa Radosavljevic, Jordan Statkic, Sasa Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia

ISBN: (纸本)9781665437783

In this paper, a hybrid metaheuristic algorithm, named the hybrid Phasor Particle Swarm Optimization and gravitational search algorithm (PPSOGSA), is proposed for estimating parameters of double-cage induction motors. The parameters are obtained by minimizing the objective function related to the error between the calculated and manufacturer data. The performances of the proposed algorithm are analyzed and evaluated using the motors of different powers. Compared to the original PSOGSA and other algorithms applied in solving the parameter estimation problem, it is found that the proposed algorithm has better performances.

关键词： induction motors parameter estimation hybrid metaheuristic algorithm Phasor Particle Swarm Optimization gravitational search algorithm

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Digital overcurrent relays coordination in renewable microgrids utilizing several operating characteristics using educational competition optimizer

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Energy Reports 2025年 13卷 6251-6266页

作者： Abdelmonem Draz Mohammed H. Alqahtani Ali S. Aljumah Attia A. El-Fergany Abdullah M. Shaheen Electrical Power and Machines Department Faculty of Engineering Zagazig University Zagazig 44519 Egypt Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electrical Engineering Faculty of Engineering Suez University Suez 43221 Egypt

Renewable microgrids (MGs), characterized by bi-directional power flow, require enhanced attention in designing protection schemes, particularly in the coordination of overcurrent relays (OCRs). Accordingly, a fresh metaheuristic algorithm named educational competition optimizer (ECO) is proposed in this study to solve the OCRs coordination problem with various operation scenarios among digital relays. Additionally, two fitness functions are incorporated into the optimization mechanism considering several relay operating characteristics (CCs) along with continuous current pickup and time dial. In this context, the programmed simulation model is validated rigorously against two other metaheuristic optimizers: artificial bee colony (ABC) and dung beetle optimizer (DBO). Initially, the effectiveness of the ECO is assessed on the IEEE 15-bus benchmark network prior to validating the proposed methodology in the IEC benchmark MG. Considering all operational scenarios, ECO attempts the best shot in minimizing the total operating time (TOT) of OCRs in both investigated networks. For instance, the ECO attains TOT of 3.2704 s and 6.3968 s with various operating CCs for 15-bus and IEC MG respectively. Undoubtedly, ECO earns this rival for all scenarios compared to ABC and DBO besides additional 16 metaheuristic literature algorithms. It is worth highlighting that when optimizing the OCR’s curve among 17 CCs, TOT is enhanced by 64.9 % and 43 % compared to fixed standard curve for 15-bus and MG respectively. Eventually, ECO consistently proves its superiority over other published optimizers in addressing the complex problem of OCR’s coordination especially in renewable MGs.

关键词： ANSI American National Standards Institute DG Distributed Generation RESs Renewable Energy Sources TOT Total Operating Time ORC Optimal Relay Coordination DOCRs Directional Overcurrent Relays OC Overcurrent FA Firefly algorithm AI Artificial Intelligence GA Genetic algorithm IEEE Institute of Electric and Electronic Engineers FF Fitness Function PSO Particle Swarm Optimizer NSGA Non-dominated Sorting Genetic algorithm IEC International Electrotechnical Commission MG Microgrid HAS Harmony search algorithm SQP Sequential Quadratic Programming HHO Harris Hawks Optimizer DE Differential Evolution GWO Grey Wolf Optimizer ABC Artificial Bee Colony GSA gravitational search algorithm ECO Educational Competition Optimizer DBO Dung Beetle Optimizer P/B Primary/Backup pairs CCs Characteristics WTGs Wind Turbine Generators EGWO Enhanced Grey Wolf Optimizer MEFO Modified Electromagnetic Field Optimizer SMA Slime Mould algorithm MFA Modified Firefly algorithm NI Normal Inverse SI Standard Inverse VI Very Inverse EI Extremely Inverse MI Moderately Inverse LI Long Inverse DI Definite Inverse OLF Over Loading Factor WCA Water Cycle algorithm MWCA Modified Water Cycle algorithm ETAP Electrical Transient Analyzer Program DIgSILENT PF DIgital SImuLation of Electrical NeTworks Power Factory Optimal coordination Overcurrent relays Metaheuristic optimizers Distributed generation Microgrids protection

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A hybridization approach of PSO and GSO algorithm for minimum-BER based multi-user detection in STBC-MIMO MC-CDMA systems

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第21-23期80卷 31967-31992页

作者： Sreesudha, P. Malleswari, B. L. GNITS Dept Elect & Telemat Engn Hyderabad India Sridevi Womens Engn Coll Hyderabad India

The usage of productive baseband receivers are portrayed by reasonable computational load is an urgent point in the advancement of transmission frameworks misusing assorted qualities in various *** research works have been published in the literature ofspace-time block codes multi-carrier code-division multiple-access (STBC MC-CDMA)Systems based on minimum conditional bit error rate Criterion. As like, in our previous paper, an efficient STBC communication system was performed using oppositional krill herd algorithm (OKHA). To overcome the drawbacks in the previous work, in this work we proposed a hybrid Particle swarm optimization (PSO) and group search optimization (GSO), called PSGSO based Multi-User Detection in STBC MC-CDMA Systems. In this paper, we use the Alamouti's STBC encoder for transmitting sequence and the channel estimation matrix is optimally evaluated using PSGSO algorithm. The simulation results show that the proposed algorithm is properly reducing the bit error rate (BER), symbol error rate (SER) and properly increase the spectral efficiency value compare to other techniques.

关键词： gravitational search algorithm Particle swarm optimization STBC Baseband receivers CDMA system Bit error rate

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A Hybrid PPSOGSA algorithm for Optimal Volt/VAr/THDv Control in Distorted Radial Distribution Systems

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APPLIED ARTIFICIAL INTELLIGENCE 2021年第3期35卷 227-246页

作者： Milovanovic, Milos J. Radosavljevic, Jordan N. Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia

This paper presents a new hybrid PPSOGSA algorithm based on the phasor particle swarm optimization (PPSO) and gravitational search algorithm (GSA) for solving the Volt/VAr/THDv control optimization problem in radial distribution systems with non-linear loads. The PPSOGSA's performance is analyzed and evaluated using the standard IEEE 30-bus radial distribution test system. The optimal solutions are obtained based on the following three optimization criteria: minimization of total active power losses, minimization of the voltage deviation, and a simultaneous minimization of total active power losses and voltage deviation. The voltage total harmonic distortion levels (THDv) are estimated using the decoupled harmonic power flow (DHPF) algorithm. Simulation results, obtained using the proposed PPSOGSA-based approach, are compared with those obtained using the other optimization algorithms such as PSO, PPSO, GSA, and PSOGSA. It is shown that the PPSOGSA-based approach has better performance in comparison to the other optimization techniques.

关键词： optimal reactive power flow harmonic distortion voltage control hybrid algorithm phasor particle swarm optimization gravitational search algorithm

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GSA improvement via the von Neumann stability analysis

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NATURAL COMPUTING 2021年第3期20卷 471-511页

作者： Naas, Ihcene Kessentini, Sameh Univ Sfax Fac Sci Sfax Lab Probabil & Stat BP 1171 Sfax Tunisia

The performance of the gravitational search algorithm (GSA) depends on the gravitational constant G, which controls the balance of exploration and exploitation abilities. Improving the setting of this parameter has attracted many researchers. In this paper, we analyzed the GSA stability using the von Neumann stability criterion. First, we modeled the iterative process by a second-order differential equation and derived the first and second-order stability conditions. Then, based on these criteria, we suggested a new law to adjust the initial value of the parameter G, depending on the distance between objects and then on the search space. Some supporting simulations were carried out using different update laws of the gravitational constant (e.g., exponential, log-sigmoid, linear, and chaotic) on CEC 2017 benchmark functions in different search space dimensions. The achieved results show that the new setting leads to significantly better outcomes in high-dimensional search spaces (greater than 20). A comparison with other metaheuristics (Particle Swarm Optimization, Artificial Bee Colony, and Grey Wolf Optimizer) reveals that the new setting proffers GSA competitiveness. Tests on 23 real-world problems (CEC 2011 benchmark problems and the iron ores sintering problem) further proved all the merits of the proposed parameter setting.

关键词： gravitational search algorithm gravitational constant Metaheuristic algorithms Parameter setting Stability analysis

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An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

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APPLIED SOFT COMPUTING 2021年 98卷 106742-106742页

作者： Ezzat, Dalia Hassanien, Aboul Ella Ella, Hassan Aboul Cairo Univ Fac Comp & Artificial Intelligence Giza Egypt Cairo Univ Fac Vet Med Microbiol Dept Giza Egypt

In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive. Published by Elsevier B.V.

关键词： SARS-CoV-2 Deep learning Convolutional neural networks Transfer learning gravitational search algorithm Hyperparameters optimization

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Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images

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MEDICAL ENGINEERING & PHYSICS 2024年 123卷 104077-104077页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha GLA Univ Dept Comp Engn & Applicat Mathura India Galgotias Univ Sch Comp Sci & Engn Greater Noida 226001 Uttar Pradesh India Uttar Pradesh Rajarshi Tandon Open Univ Dept Phys Prayagraj Uttar Pradesh India

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace;111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss.

关键词： Glaucoma screening gravitational search algorithm Optimization Feature selection Computer -assisted diagnosis

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Combination of predictive models with an optimal adaptive fuzzy controller for active suspension systems having control force constraints on front and rear tires

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第4期43卷 862-874页

作者： Mahmoodabadi, Mohammad Javad Javanbakht, Mohammad Sirjan Univ Technol Dept Mech Engn Sirjan Iran

This paper presents optimal adaptive fuzzy approaches combined by the predictive models for five degrees of freedom vehicle systems having a control force constraint of 1000 N on both front and rear suspensions in order to minimize the road disturbances. First, two separate adaptive fuzzy controllers are designed for the rear and front tires using the singleton fuzzifier, center average defuzzifier and product inference engine. The constructed fuzzy systems implement the adaptation laws based on the Lyapunov theory to guarantee the stability of the system. Afterward, a gravitational search optimization algorithm is applied to calculate the optimal values of the controller's gains. The weighted summation of four objectives, as the relative displacement between the sprung mass and the front tire, the relative displacement between the sprung mass and the rear tire, the acceleration of the body and the acceleration of the seats, are regarded in the optimization process. Two different predictive models are employed to find the optimal design variables for the circumstances where the stability of the system is under variation. The first model is a fuzzy predictive system while the second one is based on the moving least squares interpolation. Eventually, the resultant online models are compared with the offline systems when the vehicle mass varies. These simulations obviously illustrate the efficiency and ability of the suggested strategy to remove the effect of the road disturbances on the ride comfort.

关键词： Moving least squares predictive model fuzzy predictive model optimal adaptive fuzzy controller gravitational search algorithm five degrees of freedom suspension system

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