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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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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Capability-based IoT access control using blockchain

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Digital Communications and Networks 2021年第4期7卷 463-469页

作者： Yue Liu Qinghua Lu Shiping Chen Qiang Qu Hugo’Connor Kim-Kwang Raymond Choo He Zhang School of Computer Science and Engineering University of New South WalesAustralia Data61 CSIROAustralia Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesChina Department of Information Systems and Cyber Security University of Texas at San AntonioUSA Software Institute Nanjing UniversityChina

Internet of Things(IoT)devices facilitate intelligent service delivery in a broad range of settings,such as smart offices,homes and ***,the existing IoT access control solutions are mainly based on conventional identity management schemes and use centralized *** are knowm security and privacy limitations with such schemes and architectures,such as the single-point failure or surveillance(e.g.,device tracking).Hence,in this paper,we present an architecture for capability-based IoT access control utilizing the blockchain and decentralized identifiers to manage the identity and access control for IoT ***,we propose a protocol to provide a systematic view of system interactions,to improve *** also implement a proof-of-concept prototype of the proposed approach and evaluate the prototype using a real-world use *** evaluation results show that the proposed solution is feasible,secure,and scalable.

关键词： Blockchain Internet of things Capability-based access control Identity management Architecture design

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms

Successful Integration of IoT and Blockchain Technologies Us...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： C. Calarany M Indumathy P. Senthilraja D. Suganya V Gopi Vardhan Yelisetty Shanmukh Rahul Department of Computer Science Tagore Government Arts and Science College Puducherry India Department of Information Technology Rajiv Gandhi College of Engineering & Technology Kirumampakkam India Department of Computer Science K.S.Rangasamy College of Technology Tamil Nadu India Department of Computer Science Rajiv Gandhi College of Engineering & technology Kirumampakkam Data science and business systems SRM Institute of Science and Technology Kattankulathur India

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction.

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One-Shot Reachability Analysis of Neural Network Dynamical systems

arXiv

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arXiv 2022年

作者： Chen, Shaoru Preciado, Victor M. Fazlyab, Mahyar Department of Electrical and Systems Engineering University of Pennsylvania United States Mathematical Institute for Data Science Johns Hopkins University United States

The arising application of neural networks (NN) in robotic systems has driven the development of safety verification methods for neural network dynamical systems (NNDS). Recursive techniques for reachability analysis of dynamical systems in closed-loop with a NN controller, planner or perception can over-approximate the reachable sets of the NNDS by bounding the outputs of the NN and propagating these NN output bounds forward. However, this recursive reachability analysis may suffer from compounding errors, rapidly becoming overly conservative over a longer horizon. In this work, we prove that an alternative one-shot reachability analysis framework which directly verifies the unrolled NNDS can significantly mitigate the compounding errors for a general class of NN verification methods built on layerwise abstraction. Our analysis is motivated by the fact that certain NN verification methods give rise to looser bounds when applied in one-shot than when applied recursively. In our analysis, we characterize the performance gap between the recursive and one-shot frameworks for NNDS with general computational graphs. The applicability of one-shot analysis is demonstrated through numerical examples on a cart-pole system. © 2022, CC BY-NC-ND.

关键词： Numerical methods

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Algorithmic Pluralism A Structural Approach To Equal Opportunity

arXiv

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arXiv 2023年

作者： Jain, Shomik Suriyakumar, Vinith M. Creel, Kathleen A. Wilson, Ashia C. Institute for Data Systems and Society MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Philosophy and Religion Khoury College of Computer Sciences Northeastern University United States

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin’s theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each decision-point or "bottleneck" limits access to opportunities with some degree of severity and legitimacy. We adopt Fishkin’s structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In our proposal of algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic *** Codes 68-06 © 2023, CC BY.

关键词： Decision making

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Evaluation of Wireless Sensor Networks Module using IoT Approach

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： L. Anand Padmalal S J. Seetha R. Juliana PS Naveen Kumar Gayatri Parasa Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattanguluthur Department of Computer Science and Engineering Mangalam College of Engg Kerala Department of Computer Science and Business Systems Panimalar Engineering College Chennai Department of Information Technology Loyola-ICAM College of Engineering and Technology Chennai India Department of CSE-DATA SCIENCE St. Ann's College Of Engineering And Technology Chirala AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.

关键词： Temperature sensors Wide area networks Vibrations Temperature measurement Wireless sensor networks Wireless networks Transceivers

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Graph Prompt Learning: A Comprehensive Survey and Beyond

arXiv

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arXiv 2023年

作者： Sun, Xiangguo Zhang, Jiawen Wu, Xixi Cheng, Hong Xiong, Yun Li, Jia Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Hong Kong University of Science and Technology Guangzhou China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by https: //***/WxxShirley/Awesome-Graph-Prompt, and https://***/sheldonresearch/ProG, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Websites

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Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference

arXiv

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arXiv 2022年

作者： Banerjee, Debangshu Ghosh, Avishek Chowdhury, Sayak Ray Gopalan, Aditya Department of Electrical and Communication Engineering Indian Institute of Science India Halicioglu Data Science Institute University of California United States Division of Systems Engineering Boston University United States

We present a non-asymptotic lower bound on the spectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behaved curvature. Specifically, we show that the minimum eigenvalue of the expected design matrix grows as Ω(√n) whenever the expected cumulative regret of the algorithm is O(√n), where n is the learning horizon, and the action-space has a constant Hessian around the optimal arm. This shows that such action-spaces force a polynomial lower bound on the least eigenvalue, rather than a logarithmic lower bound as shown by Lattimore and Szepesvari (2017)for discrete (i.e., well-separated) action spaces. Furthermore, while the latter holds only in the asymptotic regime (n→∞), our result for these "locally rich" action spaces is any-time. Additionally, under a mild technical assumption, we obtain a similar lower bound on the minimum eigen value holding with high probability. We apply our result to two practical scenarios – model selection and clustering in linear bandits. For model selection, we show that an epoch-based linear bandit algorithm adapts to the true model complexity at a rate exponential in the number of epochs, by virtue of our novel spectral bound. For clustering, we consider a multi agent framework where we show, by leveraging the spectral result, that no forced exploration is necessary—the agents can run a linear bandit algorithm and estimate their underlying parameters at once, and hence incur a low regret. © 2022, CC BY.

关键词： Eigenvalues and eigenfunctions

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Deep Learning and MRI Improve Carotid Arterial Tree Reconstruction

Deep Learning and MRI Improve Carotid Arterial Tree Reconstr...

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data science and Network Security (ICDSNS), International Conference on

作者： Narasimha Murthy M S Gunti Spandan Inakollu Aswani Shankar Nayak Bhukya Ramachandra A C Department of Information Science & Engineering BMS Institute of Technology & Management Bengaluru India Department of Computer Science GST GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of CSE (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

This study suggests using magnetic resonance imaging (MRI) scan data to segment and rebuild the carotid artery tree using a deep learning-based method. In the proposed method, the UNET architecture is used to divide an image into parts, and then a 3D level set method is used to make the parts even better. The method was compared to seven different approaches to segmentation and reconstruction, and it outperformed all but one of them. It had a 94% accuracy rate and took 12 hours to process. Deep learning's capacity to reliably segment and categorise pictures, the UNET architecture's ability to maintain spatial information through skip connections, and the level set approach's ability to improve segmentation further all contribute to the proposed method's effectiveness. The proposed approach has the potential to be a useful tool for clinicians in evaluating the status of the carotid artery and finding any irregularities. Stroke is one of the most devastating symptoms of cardiovascular disease, and recognising and treating carotid artery stenosis in its early stages can help avoid or reduce the risk of having a stroke. The success of the proposed method shows that deep learning techniques have the potential to improve the accuracy of carotid arterial tree reconstruction from MRI data. Better identification and treatment of carotid artery stenosis may increase the overall effectiveness of the proposed technique, ultimately leading to better patient outcomes.

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