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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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A taxonomy and survey of big data in social media

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Concurrency and Computation: Practice and Experience 2024年第1期36卷

作者： Hemmati, Atefeh Arzanagh, Hanieh Mohammadi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan

Examining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high-quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data-trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1-score, and 5% run time complexity. © 2023 John Wiley & Sons, Ltd.

关键词： Machine learning

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Deep Learning Approaches for Environmental Monitoring in Smart Cities 9

Deep Learning Approaches for Environmental Monitoring in Sma...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Latha Soundarraj, Prem Jamadar, Vahid Mansur Rappai, Sherin Alkhafaji, Mohamed Ayad Shareef, Ashraf Mohammed Zearah, Sajad Ali School of Business & Management - Mba Christ University Maharashtra Pune412112 India Dr. DaulatraoAher College of Engineering Department of Mechanical Engineering Satara Maharashtra Karad415124 India Department of Computer Science Kothanur K. Narayanapura Karnataka Bangalore560077 India College of Engineering Technology National University of Science And Technology DhiQar Iraq National University of Science And Technology DhiQar Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq

ISBN: (纸本)9798350365092

It introduces a novel integrated environmental monitoring system capable of doing on-the-go measurements. In metropolitan settings, air pollution is one of the most serious environmental threats to human health. The widespread use of automobiles, emissions from manufacturing processes, and the use of fossil fuels for propulsion and power generation have all contributed to this issue. Air quality predictions in smart cities may now be made using deep learning methods, thanks to the widespread adoption of these tools and their continued rapid growth. Particulate Matter (PM) with a width of less than 2.5 m (PM2.5) is one of the most perilous kinds of air pollution. To anticipate the hourly gauge of PM2.5 focus in Delhi, India, we utilized verifiable information of poisons, meteorological information, and PM2.5 fixation in the adjoining stations to make a spatial-worldly element for our CNN-LSTM-based deep learning arrangement. According to our experiments, our 'hybrid CNN-LSTM multivariate' method outperforms all of the above conventional models and allows for more precise predictions. © 2024 IEEE.

关键词： Air quality

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The Optimal Shape of the Surface Hardening Head Obtained by Numerical Modeling 15

The Optimal Shape of the Surface Hardening Head Obtained by ...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Mihaela Cornelia Codrean, Marius Gordan, Mircea Codrean, Mihaela Oproescu, Mihai Novac, Ovidiu Constantin Slovac, Francisc University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Computers and Electrical Engineering University of Pitesti Faculty of Electronics Communication and Computer Science Department of Elecytronics Pitesti Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania

ISBN: (纸本)9798350321388

This paper presents some aspects of the numerical modeling performed in order to study the process of induction heating applied to a workpiece, using Flux 2D and FEMM (Finite Element Method Magnetics), two software tools specifically designed for electromagnetic simulations. The paper presents a significant technical finding regarding the optimal shape of the surface hardening head. Various shapes of the inductor were analyzed, focusing on solving the eddy current problem using the commercial program FEMM. The induction heating process was numerically simulated using the FLUX 2D program for a semi-finished punch made of OLC 45 steel, specifically for hot surface heat treatment. The simulations were conducted utilizing the program's simulation capabilities. © 2023 IEEE.

关键词： Induction heating

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The Peridigm Meshfree Peridynamics Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第1期6卷 118-148页

作者： Littlewood, David J. Parks, Michael L. Foster, John T. Mitchell, John A. Diehl, Patrick Center for Computing Research Sandia National Laboratories Albuquerque NM United States Hildebrand Department of Petroleum and Geosystems Engineering University of Texas at Austin Austin TX United States Center of Computation & Technology and Department of Physics and Astronomy Louisiana State University Baton Rouge LA United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN United States

Peridigm is a meshfree peridynamics code written in C++ for use on large-scale parallel computers. It was originally developed at Sandia national Laboratories and is currently managed as an open-source, community driven software project. Its primary features include bond-based, state-based, and non-ordinary state-based constitutive models, bond failure laws, contact, and support for explicit and implicit time integration. To date, Peridigm has been used primarily by methods developers focused on solid mechanics and material failure. Peridigm utilizes foundational software components from Sandia’s Trilinos project and was designed for extensibility. This paper provides an overview of the solution methods implemented in Peridigm, a discussion of its software infrastructure, and demonstrates the use of Peridigm for the solution of several example problems. © 2023, The Author(s).

关键词： Meshfree methods Open source software Peridynamics

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Latency Correction for Event-Guided Deblurring and Frame Interpolation

Latency Correction for Event-Guided Deblurring and Frame Int...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yixin Yang Jinxiu Liang Bohan Yu Yan Chen Jimmy S. Ren Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University SenseTime Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Event cameras, with their high temporal resolution, dynamic range, and low power consumption, are particu-larly good at time-sensitive applications like deblurring and frame interpolation. However, their performance is hindered by latency variability, especially under low-light conditions and with fast-moving objects. This paper addresses the challenge of latency in event cameras - the temporal discrepancy between the actual occurrence of changes in the corresponding timestamp assigned by the sensor. Focusing on event-guided deblurring and frame interpolation tasks, we propose a latency correction method based on a parameterized latency model. To enable data-driven learning, we develop an event-based temporal fidelity to describe the sharpness of latent images reconstructed from events and the corresponding blurry images, and reformulate the event-based double integral model differentiable to latency. The proposed method is validated using synthetic and real-world datasets, demonstrating the benefits of latency correction for deblurring and interpolation across different lighting conditions.

关键词： Interpolation Power demand Computational modeling Lighting Focusing Dynamic range Cameras

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An optimal on-demand scrubbing solution for read disturbance errors in phase-change memory

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IEIE Transactions on Smart Processing and Computing 2021年第1期10卷 55-60页

作者： Kim, Moonsoo Lee, Juhan Kim, Hyun Lee, Hyuk-Jae Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Republic of

Phase-change memory is a promising technology due to its attractive properties. However, phase-change memory is difficult to commercialize because of its reliability issues. A read disturbance error, which is the main cause of reliability issues, occurs when a cell is repeatedly read. A conventional solution for read disturbance errors is periodically scrubbing the cells. However, this method requires read counters to count the number of reads per word. This paper proposes an on-demand scrubbing solution that does not require read counters, which significantly reduces resource overhead. The proposed method observes the number of errors in a word using error-correcting code. If the number of errors is larger than a pre-defined threshold, scrubbing is performed to fix the errors. The proposed method removes nearly 1GB of hardware overhead required by read counters, and fixes more than 99.99% of read disturbance errors. © 2021 The Institute of Electronics and Information Engineers

关键词： Phase change memory

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RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation 39

RETIA: Relation-Entity Twin-Interact Aggregation for Tempora...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Kangzheng Zhao, Feng Xu, Guandong Wang, Xianzhi Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China University of Technology Sydney Data Science and Machine Intelligence Lab Sydney Australia

ISBN: (纸本)9798350322279

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations;therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands"problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://***/CGCL-codes/RETIA. © 2023 IEEE.

关键词： Extrapolation

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Effects of Drain Field Plate Structure and Passivation Dielectrics on Breakdown Voltage of GaN MISHEMT

Effects of Drain Field Plate Structure and Passivation Diele...

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2023 IEEE Workshop on Wide Bandgap Power Devices and Applications in Asia, WiPDA Asia 2023

作者： Langpoklakpam, Catherine Hsiao, Yi-Kai Lin, Chun-Hsiung Kuo, Hao-Chung National Yang-Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics and Institute of Electro-Optical Engineering Hsinchu City Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei Taiwan National Yang-Ming Chiao Tung University International College of Semiconductor Technology Hsinchu City Taiwan

ISBN: (纸本)9798350337112

In this work, a novel D-mode GaN MISHEMT with two steps drain field plate is proposed. The designed two steps drain field plate modulates the high electric field at the drain side by spreading the electric field across the two field plates of the device thus enhancing the breakdown voltage of the device. The breakdown voltage of the device is about 1587V which is 350V more than that of the conventional GaN MISHEMT. Furthermore, it is worth noting that the adoption of the proposed design shows minimal impact on the DC characteristics of the device, ensuring its overall performance remains largely unaffected. In addition, by using high-k passivation layers the breakdown of the conventional device is improved by 20.6 % by using HfO2 and reduced by S.4 % by using SiO2 when compared with the SiN passivation layer. © 2023 IEEE.

关键词： Electric breakdown

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