Network optimization papers. Instead of … Mathematical Programming, 1997.

Network optimization papers 33245. We explore various AI techniques for network optimization, including traffic prediction, anomaly detection, resource allocation, and automated network maintenance. Such problems are characterized by the presence of one or more objective maximizing or minimizing functions [5] and various restrictions that must be met so that the solution is valid. Queuing is one of the most usable tools that help in analyzing the performance of complex telecommunication and system networks. Similarly, if all its arcs are undirected, the network is said to be an undirected network. To address this issue, this paper proposes an adaptive dynamic routing framework that decouples demand perception from decision-making, dynamically configuring network Dec 3, 2022 · Optimization of electricity surplus is a crucial element for transmission power networks to reduce costs and efficiently use the available electricity across the network. The QAB is a four-port converter capable of handling 3 days ago · Natural gas pipeline network simulation technology is the fundamental technology of system capacity analysis, pipeline design, operation planning and optimization as well as emergency decision‐making for the whole life cycle of a given pipeline Oct 8, 2020 · In this paper, two-stage risk-averse and risk-neutral stochastic optimization models are proposed to schedule repair activities for a disrupted CI network with the objective of maximizing system Jan 1, 2012 · In this paper, we provide a survey on the state of the art of WAN optimization or WAN acceleration techniques, and illustrate how these acceleration techniques can improve application performance There is an increasing demand of real-time routing optimazation using Sotware Defined Networking (SDN) for better Quality of Service. G2 generates an interactive graph structure that describes how perturbations in links and flows propagate, providing operators new In this paper, we propose a novel deep reinforcement learning (DRL) algorithm, called Advantage Actor Critic-Graph Searching (A2C-GS), for network topology optimization. In this paper, we develop one such algorithm that runs in O(min(n 2 m log nC, n 2 m 2 log n)) time, where n is the number of nodes in the network, m is the number of arcs, and C denotes the maximum absolute arc costs if arc Aug 1, 2020 · Network optimization has generally been focused on solving network flow problems, but recently there have been investigations into optimizing network characteristics. View a PDF of the paper titled Network connectivity optimization: An evaluation of heuristics applied to complex networks and a transportation case study, by Jeremy Auerbach Dec 19, 2014 · Abstract page for arXiv paper 1412. The IoF framework allows for finding an exemplary resource allocation configuration of the mobile network by leveraging SDN technology. Jan 1, 2023 · In this paper, we focus on neural network compression from an optimization perspective and review related optimization strategies. We first present a base model that aims to determine the strategic ‘location’ and tactical ‘allocation’ decisions for a deterministic four-tier supply chain. Recent advances in data structures, computer technology, and algorithm development have made it possible to solve classes of Jan 1, 2016 · H ow to cite this paper: Douiri, L. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous opti-mization. His research interests include network optimization, mathematical This paper focuses exclusively on AI techniques applied to routing in next-gen networks, exploring the reasons behind AI’s significance in network optimization. This document provides a detailed description of the tutorial lecture on Challenges in Network Optimization provided at the 2016 IEEE 17th International Conference on High Performance Switching and Routing (HPSR). Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. Diversity and flexibi May 29, 2024 · wireless networks. The rest of the paper is divided as follows: Section 2 introduces the underlying models used in the paper and derives a representation for the point-to-point traffic. With the powerful tools now available in complex network theory for the study of network topology, it is This paper focuses on operations research network optimization models, detailing the application of various graph-based problems such as the maximum flow problem, minimum cost network flow problem, and minimum spanning tree problem. Specifically, we summarize optimization techniques emerging from four general categories of commonly used network compression approaches, including network pruning, low-bit quantization, low-rank factorization, and Jan 1, 2023 · In this paper, we focus on neural network compression from an optimization perspective and review related optimization strategies. Jan 23, 2023 · to-end learning for semantic optimization, for solving challenging large-scale optimization problems arising from various important wireless applications. In this paper, we present a new traffic control Dec 1, 2023 · This Special Issue focuses on advanced and novel optimization theory and algorithms for next generation wireless communication networks, aiming at bringing together researchers and industry practitioners working in related areas to share their new ideas, latest findings, and state-of-the-art results. Nov 29, 2016 · This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. Jan 23, 2024 · The use of nonlinear active queue management algorithm in the computer network connection enhancement optimization process can effectively analyze the packet loss rate and queue length in detail, and can effectively solve the phenomenon of anomalies at the maximum and minimum thresholds in the network packet loss process []. More importantly, this white paper will suggest a change to the environment the RF optimization can become a network function that operates on the existing baseband server, making the implementation much simpler and Jan 4, 2024 · paper, we propose Optimizing Convolutional Neural Network Architecture (OCNNA), a novel CNN optimization and construction method based on pruning and knowledge distillation designed to establish the importance of convolutional layers. We think optimization for neural networks is an interesting topic for theoretical research due to various reasons. , based on predicted future traffic patterns [2]. Through an in-depth examination of the methodologies, techniques, applications, challenges, and future in network optimization is dynamic routing, where reinforcement learning agents learn to dynamically adjust May 26, 2023 · The main goal of machine learning is the creation of self-learning algorithms in many areas of human activity. 1 Publications Included in the Thesis 24 4. The Feb 13, 2015 · Uncertain network optimization is the study of network optimization with uncertain data which we often meet in decision making under the presence of uncertainties. 1, we present related research and show the extent to which optimization models ensure supply reliability as well as the effectiveness in terms of the size of solved distribution networks. First, as newly strategic analysis of the emerging online marketplace considering risk attitude and channel power, Jun 12, 2013 · Network Optimization Problems Subject to Max-Min Fair Flow Allocation @article{Amaldi2013NetworkOP, title={Network Optimization Problems Subject to Max-Min Fair Flow Allocation}, author={Edoardo Amaldi and Antonio Capone and Stefano Coniglio and Luca Giovanni Gianoli}, journal={IEEE Communications Letters}, year={2013}, volume={17}, Aug 10, 2021 · Figure 1: Three types of networks: (a) Tree; (b) Simple network; and (c) Complex network. Specifically, we introduce a connectivity-preserving crossover operator to Aug 15, 2024 · This paper investigates the application of AI-driven solutions to enhance network performance and Quality of Service (QoS) in future telecommunications. The foundational work Apr 1, 2023 · Hence, optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks. All contributions will be peer Feb 13, 2025 · Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning A new book in preparation; Athena Scientific. In Sect. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. He is the author or co-author of more than 100 journals and conference papers, and he is the originator of the Net2Plan open-source initiative which includes the Net2Plan tool and public repository of algorithms and network planning resources (www. 1 Spiking neural network models Spiking neural networks (SNNs) can be modeled with a broad range of neuron models. multi-commodity, multimodality freight transport network optimization model. In this paper, we review six types of optimization methods - they are Lyapunov optimization, convex optimization, heuristic techniques, game theory, machine learning, and others. Feb 25, 2025 · network_lasso. To enable ML implementation in dis-tributed wireless networks across massive number of end devices, federated learning for distributed optimization will further be presented. In such a complex environment, resource optimization, and security Jan 28, 2025 · Network Optimization Data Manipulation, Model Architecture, Hyperparameter Optimization Swathi Jadav | Vishhvak Srinivasan. First, we define the QUBO problem for the partitioning of the network, and test the In this paper we present G2, the first operational network optimization framework that utilizes this new theoretical framework to characterize with high-precision the performance of bottlenecks and flows. Fractional order, bilevel, and gradient-free optimizers can replace classical gradient Apr 21, 2021 · Various kinds of network optimization problems appear in many fields of work, including telecommunication systems, commodity transportation, railroad and highway traffic planning, electrical power distribution, and much more. The theory of artificial neural networks, which have already replaced humans in many problems, remains the most well-utilized branch of machine This paper proposes a DRL based network resource allocation algorithm for multi-objective optimization problems in network resource allocation. A. Simulation results verify that the proposed synergetic optimization method can obviously improve power quality and decrease network loss. First, we define the Quadratic Unconstrained Binary Optimization Mar 2, 2025 · This paper presents a conceptual review of recent key advancements in this emerging field, aiming at optimization and machine learning researchers. DIFFERENT NETWORK MODELS AND OPTIMIZATION PROBLEMS Network models can be viewed in various prospective based on the research topic on interest. 2. Keywords: Combinatorial optimization, graph neural networks, reasoning ©2023 Quentin Cappart, Didier Ch´etelat, Elias B. and El Barkany, A. , Jabri, A. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e. DRL-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating Mar 26, 2022 · IoT network optimization has several advantages, including better traffic control, operational reliability, energy saving, lower latency, higher throughput, and a quicker rate of scaling up or installing IoT services and devices. Mar 1, 2017 · OptNet is presented, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks, and shows how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers. The comparison exercise deals with clearly defined automatic mapping scenarios. pdf. AdaptiveDNNinference with early exiting leverages the observation that some test examples can be easier to predict than others. 11 AC, b/g/n, P and other wireless network performance of mesh routing protocol, and the design and experiment of wireless Mesh network, the experiment with network coverage and attenuation Nov 3, 2021 · In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e. In this paper, the minimum active power loss is taken as the objective to model the distribution network with distributed generations, and the extremum optimization algorithm is introduced to improve it. , air and railway), brain networks, and paper citation networks [23]. This paper establishes a reasonable network Mar 7, 2025 · An optimization model with the aim of reducing voltage deviation, network loss, and the ratio of PV abandonment was constructed. The model Oct 12, 2021 · In this paper, we investigate state-of-the-art techniques in machine intelligence-enabled network routing and discuss the development trends of machine intelligence-enabled routing optimization techniques for the future network. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of Jun 1, 2019 · In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. These methods are surveyed on different networks and surroundings such as wired networks, wireless networks Nov 28, 2024 · In this paper, a novel approach is proposed to reduce control energy by rewiring networks while keeping the number of driver nodes unchanged. This Special Issue explores innovative approaches to programmable and reconfigurable networks, focusing on performance and sustainability. Differently from traditional approaches to control traffic signaling, our simplified framework allows a more tractable analysis of the network dynamics and, yet, accurately Dec 11, 2024 · This white paper examines Virtualization and Open RAN architectures, to illustrate the level of performance we can expect. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), 3 days ago · Network optimization is achieved through a combination of cognitive software technologies which are uniquely developed and applied by software engineers and data scientists across the full end-to-end network and the various lifecycle stages of planning, design, tuning and continuous optimization. in 2020) proposed in their paper decomposable Winograd algorithm (DWM) to extend the minimal filtering algorithm to dilated convolutions where the kernel size is greater than 3 Optimization of neural network parameters can be achieved using most widely. In this paper, we present a close look Feb 15, 2023 · In this paper, we propose a novel deep reinforcement learning (DRL) algorithm for graph searching, called DRL-GS, for network topology optimization. Improving Customer Nov 3, 2021 · In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. Oct 18, 2016 · to determine an e cient production and distribution plan for a network of produc-tion, storage, and distribution centers given available data. In this paper we showed how to optimize such a network with quantum annealing. Network slicing is one of the core techniques of the current 5G networks. Nov 30, 2014 · This paper presents a robust optimization model for the design of a supply chain facing uncertainty in demand, supply capacity and major cost data including transportation and shortage cost parameters. This paper proposes a control optimization of a Quadruple Active Bridge (QAB) converter using a neural network. Our network optimization Oct 1, 2021 · Network connectivity optimization, which aims to manipulate net-work connectivity by changing its underlying topology, is a fun-damental task behind a wealth of high-impact The refereed papers in this volume reflect the interdisciplinary efforts of a large group of scientists from academia and industry to model and solve complicated large-scale network optimization problems. The main purpose of this manuscript is to present a state-of-the-art review on the recent advances in uncertain network optimization and to show the general uncertain network optimization models Aug 16, 2023 · nication networks. This paper focuses on distributed optimization over networks, or decentralized optimization, where each agent is only allowed to aggregate information from its neighbors over a network (namely, no centralized coordination is present Software Defined Networking (SDN) is a driving technology for enabling the 5th Generation of mobile communication (5G) systems offering enhanced network management features and softwarization. Contents II Papers 43 Paper I 47 Dec 18, 2023 · Leveraging AI and ML for Network Optimization, Predictive Maintenance, and Traffic Management in Next-Gen Networks December 2023 DOI: 10. The proposal has been evaluated though a thorough empirical study including the best known datasets (CIFAR-10 Jan 29, 2021 · This white paper gives a broad summary of what one can expect from the more -depthin roadmap effort It is the aim of the IEEE Future Networks Initiative Systems Optimization Working Group to form a scientific community to define SOSs , identify key problems, and provide solutions based on various tools The core focus of this paper will remain on complex network theory-based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. We call this new approach neural architecture optimization (NAO). This paper suggests a framework to the backbone network optimization by minimizing the network delays. Oct 31, 2024 · Call for Papers "Recent Advances in Network Optimization"- EURO 2024. In this section, we discuss different views of these network models. Developing a polynomial time algorithm for the minimum cost flow problem has been a long standing open problem. We conclude the paper by identifying several open research challenges and outlining future research directions. Challenges and potential solutions are discussed, highlighting the Oct 22, 2021 · The second paper investigates the impact of sampling cost in a 3. This paper presents a survey of the recent efforts towards a systematic understanding of “layering” as “optimization decomposition,” where the overall communication network is modeled by Jan 1, 2023 · Transport problems are efficiently solved using a linear programming model This paper presents the optimization of the distribution network and overall transport costs of Quehenberger Logistics, where the VAM method was used, which resulted in significant savings compared to the existing network. In this paper, we show how to optimize such network costs using a quantum annealing approach. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. A network of N such neurons is described by their voltage dynamics, V_ (t) = V(t) + Fc(t) + Feb 8, 2025 · Abstract. The ties between linear programming and combinatorial optimization can be traced to the representation of the constraint polyhedron as the convex hull of its extreme points. This review paper depicts comprehensive survey on the most important aspects through some of the novel approaches related to network optimization for IoT As various Internet applications continue to demand high-speed, reliable, and efficient network performance, network administrators are faced with the challenge of optimizing network performance. , genetic algorithm (GA), particle swarm optimization Distributed generation access to distribution network will change its power flow and node voltage distribution, which may affect the safe and stable operation of distribution network.  · Explore the latest full-text research PDFs, articles, conference papers, preprints and more on NETWORK OPTIMIZATION. Network protocol stacks may instead be holistically analyzed and systematically designed as distributed solutions to some global optimization problems. The fundamental question in network optimization is how to efficiently transport some entity (data Jul 1, 2022 · Haung et al. Network modeling is a critical component of Quality of Service (QoS) optimization. Leveraging artificial intelligence (AI) algorithms such as machine learning and deep Network optimization is important in the modeling of problems and processes from such fields as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. To mitigate such problems, network optimization can be one solution. Jan 30, 2024 · The full deployment of sixth-generation (6G) networks is inextricably connected with a holistic network redesign able to deal with various emerging challenges, such as integration of heterogeneous technologies and devices, as well as support of latency and bandwidth demanding applications. tuce Golden is an Associate Professor of Management Science and Statistics at the University of Maryland. The majority of existing approaches focus on learning data-driven optimizers that lead to fewer iterations in solving an Mar 5, 2025 · The proposed TrafficKAN-GCN framework offers a promising direction for data-driven urban mobility management, balancing predictive accuracy, robustness, and computational efficiency, and compares with baseline models such as MLP-GCN, standard GCN, and Transformer-based approaches. This special issue of Networks invites high-quality and innovative submissions that focus on network optimization methods arising, for example, in logistics, routing, transportation, and network design. By combining deep learning (DL) and reinforcement learning (RL) algorithms to construct a neural network, energy efficiency is used as a reward and punishment value, historical data is saved in an To maintain a stable network interconnection in the Internet, IP network plays a critical role. As we all know, network optimization is a complex, arduous and far-reaching work. , routing), while the optimization algorithm generates configurations that can potentially meet the expected performance, for example, Dec 17, 2023 · Well-trained deep neural networks (DNNs) treat all test samplesequallyduringprediction. The rst half of this paper provides a The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. Two criteria are evaluated to make an objective comparison between the algorithms: the smallest MUKV achieved and the required computation time. net2plan. , per-path delay) for a specific configuration (e. DRL-GS consists of three Mar 21, 2013 · In this paper we optimize network flows by minimizing network blocking/delay in a general network and under stochastic point-to-point demands. reliability, and security to optimally utilize the available networks. In this paper, an implementation of network simplex algorithm is described for solving the minimum cost Jun 11, 2021 · Network Pruning via Performance Maximization Shangqian Gao1, Feihu Huang1, Weidong Cai2, and Heng Huang*1,3 1Electrical and Computer Engineering Department, University of Pittsburgh, PA, USA 2School of Computer Science, The University of Sydney, NSW 2006, Australia 3JD Finance America Corporation, Mountain View, CA, USA shg84@pitt. 6544: Qualitatively characterizing neural network optimization problems Training neural networks involves solving large-scale non-convex optimization problems. > This type of optimization problem is often solved when expanding the topology of a computer network. In Sect. J. It allows a replacement of a person with artificial intelligence in seeking to expand production. Instead of Mathematical Programming, 1997. Network’s Utilization and Optimization Management based on Performance Analysis using Queuing Theory and COMNET_III. There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. Neely, Stochastic Network Optimization With Application to Communication and Queueing Systems. edu RuiyuePeng TranslationalMRI,LLC Sep 1, 2020 · This paper surveys major attempts on reducing latency and increasing the throughput. BGP Networks Network optimization in the context of computer science refers to the technology used to improve the performance of a network by enhancing its data rate, recovery, and response time. In this paper, we take a different approach and propose to replace the iterative solvers altogether with a trainable parametric set function, that outputs the optimal arguments/parameters of an optimization The node coverage optimization problem of wireless sensor network (WSN) is a critical challenge in practical applications of WSN. Jan 7, 2023 · Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. It captures the objectives of the particular actors Leveraging machine learning to facilitate the optimization process is an emerging field that holds the promise to bypass the fundamental computational bottleneck caused by classic iterative solvers in critical applications requiring near-real-time optimization. Whereas supervised learning requires pre-solved instances for training, our approach leverages a custom loss function derived from the exact This paper proposes a framework, named intelligent optimization framework (IoF), that leverages both network optimization and machine learning techniques for achieving the best performance results. Sep 5, 2018 · 2Note that one-way TOA-based ranging requires network synchronization, but round-trip TOA-based ranging and RSS-based ranging cancircumvent the synchronization requirement. Khalil, Andrea Lodi, Christopher Morris, Petar Veliˇckovi´c. Google Scholar Apr 14, 2020 · highly dynamic wireless networks and take more intelligent decisions, e. The damping groove structure of the distribution plate plays a crucial role in the pulsation suppression, vibration reduction, and noise optimization of the piston pump. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles Oct 8, 2020 · Research published in Networks and by the broader network optimization community has focused primarily on technical methods for robustness and rebound. Apr 2, 2022 · A network that has only directed arcs is called a directed network. Urban traffic optimization is critical for improving The selection of the optimization algorithm (optimizer) is one of the most essential endeavors in Deep Learning and across all categories of Neural Networks. In computing, topology is useful to understand networks, the flow of information throughout the network, or paths within a system. Nov 1, 2023 · Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Sep 21, 2022 · The presented optimization problems for wireless networks take into consideration two key aspects: coverage and interference (Section “Coverage and Interference”) and energy consumption (Section “Energy Consumption Model”). (2016) Models for Optimization of Supply Chain Netwo rk D e- sign Integrating the Co st of Quality: A Lit erature Review . com). In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. This paper conducts a literature review to present research opportunities to solve Oct 10, 2023 · The mathematical science behind network analysis and network optimization is the graph theory. Accordingly, this paper explores and discusses the use of QML to address these challenges. A network with a mixture of directed and undirected arcs (or even all undirected arcs) can be converted to a directed network, if desired, by replacing each undirected arc by a pair of This paper proposes a simplified version of classical models for urban transportation networks, and studies the problem of controlling intersections with the goal of optimizing network-wide congestion. , [1]). Ioannis N. 13140/RG. Stemming from these observations, this paper proposes a modular ML-based wireless network optimization framework, which enables plug-and-play integration of machine intelligence into new, as well as existing, network Feb 7, 2025 · The convergence of access, edge, and cloud networks demands advanced reconfigurability, making network programmability essential for adaptation and optimization. . Feb 15, 2023 · In this paper, we propose a novel deep reinforcement learning (DRL) algorithm for graph searching, called DRL-GS, for network topology optimization. Jul 18, 2022 · the Optimization Process (LOOP), is inspired by biological systems that are capable of solving complex optimization problems upon encountering the problem multiple times. 2 Papers Not Included in the Thesis 29 Bibliography 31 xv. Jun 8, 2023 · An important feature of a bandwidth optimization system is the adequate provision of internet services with high data rates and wide coverage. Oct 24, 2024 · In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. By omitting the classic iterative solutions, LOOPovercomes one of the major optimization bottlenecks enabling near-real-time optimization in a wide range of critical applications. This paper 2 Spiking neural networks and convex optimization 2. This lecture is motivated by the recent evolutions of communication networks which have induced new challenges in mixed-integer network Feb 24, 2025 · DOI: 10. SnapVX software. 2, we conclude research gaps and situate this study within the existing literature. We model network rewiring to an optimization problem and develop a memetic algorithm to solve it accurately and efficiently. May 26, 2023 · After that, we show ways to develop optimization algorithms in further research using modern neural networks. Digital Library. 18406 May 1, 2005 · This paper will elaborate on the need for finding common grounds between optimization of telecommunication networks and optimal control theory; some existing measurement tools will be presented. This paper concentrates on reducing the operating expenditure (OPEX) costs while i) increasing the quality of service (QoS) by leveraging the benefits of queuing and multi-path Oct 23, 2020 · Distribution Network Optimization: A Case Study By Arun Bhardwaj Thesis Advisor: Dr. As a new The research content of this paper mainly includes two parts, one is the development of wireless routing protocol and the direction of future development, the other is based on the 802. The NOM solves optimization problems by extending the architecture of the NN objective function model. The model predicts the performance (e. To accommodate as many network slices as possible with limited hardware resources, service providers need to avoid over-provisioning of resources. The study delves into cognitive intelligence, knowledge planes, and the role of Software-Defined Networking (SDN). Architecture of acceleration of iterative optimization from a representative paper (Sosnovik & Oseledets, 2019). The problem of optical fiber access or the high construction cost and long construction cycle of optical fiber will lead to the problem of service access. This paper introduces a comprehensive Sep 11, 2023 · Abstract: Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole. Find methods information, sources, references or Feb 1, 2019 · The outcome of this paper highlights the importance of network optimization in IoT, with ample amount of bibliography contents desired to help new researchers embark to work Jun 8, 2023 · Therefore, this article gives a brief overview of a list of bandwidth optimization models deployed through previous researchers, stating the optimal algorithms that work with each of the models May 1, 2013 · In order to have a supply chain network or just a supply chain-operation in its best possible way, some optimization techniques are needed. Figure 5. Jun 9, 2023 · single-stage MLTO, which is MLTO process through one network, and Figure 6 shows an example of the multistage MLTO process, which is MLTO process through two or more networks. There are three phases of optimization network reconfiguration such as start This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. 2 Stochastic Network Optimization 20 4 Contributions of the Thesis 23 4. , genetic algorithm Nov 3, 2024 · A novel neural network-based approach is proposed to develop a controller which improves the performance of a Quadruple Active Bridge converter on the basis of a non-simplified model of the transformer. The performance of the PSO, the fusion and the genetic algorithms are simulated. The purpose of this monograph is to present new research and an up-to-date Aug 3, 2020 · The special issue has collected five high-quality papers which develop network optimization models or algorithms in uncertain environments or based on the background of big data. However, most CPN research focuses on task optimization during resource allocation, while Aug 1, 2024 · Complex networks, characterized by topological features that are neither purely random (as seen in random graphs [21]) nor entirely regular (as in lattice graphs [22]), are prevalent in various real-world contexts such as the World Wide Web, transportation networks (e. edu, Nov 9, 2022 · Network optimization is typically achieved by combining two main elements: (i) a network model and (i i) an optimization algorithm (e. Network optimization lies in the middle of the great divide that separates the two major types of The core focus of this paper will remain on complex network theory-based indices, optimization models, optimization methodologies, challenges, and technical issues, and discusses future direction for transmission network reconfiguration problem for grid resilience. 2. Here, we focus on arguably the most common model, leaky integrate-and-fire (LIF) neurons. 54097/c6e8rk71 Corpus ID: 276626453; Graph Neural Network Graph Embedding Optimization Method for Vascular Stress Prediction @article{Yu2025GraphNN, title={Graph Neural Network Graph Embedding Optimization Method for Vascular Stress Prediction}, author={Xin Yu and Mohan Zhang and Jiayao Li and Xinrui Wang and Shumin Jin Stochastic Optimization methods are used to optimize neural networks. Analysis on the flow constrained optimization problems suggests the possibility that a smaller-sized problem can be solved while sharing equally optimal solutions with the original problem, by excluding the Mar 1, 2024 · The purpose of this paper is to explore the potential of AI-driven optimization in enhancing network performance and efficiency. Through A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The paper analyses the distribution network of 3 days ago · The cloud-edge-terminal architecture relies on hierarchy for resource allocation but lacks global optimization. Three algorithms were used for solving the multi-objective optimization model. A2C-GS consists of three novel components, including a verifier Nov 1, 2022 · Faced with these challenges, this paper reviews the very recent mathematical and learning based techniques, including various cutting-edge technologies such as deep learning, reinforcement learning, and black-box Mar 21, 2013 · investments, backbone network optimization becomes important. Below you can find a continuously updating list of stochastic optimization algorithms. Lagoudis Summary: The present work expands the existing literature on optimizing distribution networks for neighboring countries by using the example of a company looking at redesigning its distribution network in South East Asia region. III. Thus, this term paper presents the performance measurements of computer networks with queuing Jan 1, 1995 · Taken as a whole, the models considered establish network optimization as an unusually powerful and rich modeling environment, adding evidence to justify the claim that applied mathematics, computer science, and operations research do indeed have much to offer to the world of practice. Second, classical optimization theory is far from enough Network Optimization Handbook Your Guide to a Better Network. This is valuable because it eliminates the need for manual calculation, something that is necessary when net-works include hundreds of di erent nodes. Current networks Mar 1, 2011 · The objective of this paper is to compare different optimization algorithms in practice. The Seven Bridges of Konigsberg is similar to another common problem in network optimization called Traveling Salesman Problem (TSP), Feb 28, 2024 · This paper delves into the potential of AI-driven optimization techniques in addressing this imperative. The Special Issue aims to foster a fruitful exchange of Jun 7, 2023 · Network optimization lies in the middle of the great divide that separates the two major types of optimization problems, continuous and discrete. We consider synchronous networks and the broadcast mode for anchor transmission in this paper, and the results can be extended to asynchronous networks. In this paper, in order to solve the pro Wireless Sensor Network Coverage Optimization Based on an Improved Pied Kingfisher Aug 7, 2022 · The optimization process is conducted by the neural network's built-in backpropagation algorithm. The computer network structure Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction . It's a matter of experimenting by making mistakes and learning from them. San Rafael, CA, USA: Morgan & Claypool, 2010. The hybrid optimization problem is formulated to provide a general theoretical framework for the analysis of a class of traffic control problems which takes May 1, 2015 · This paper presents a freight transport optimization model that simultaneously incorporates multimodal infrastructure, hub-based service network structures, and the various design objectives of multiple actors. It is proposed that public transit network can be abstracted into series-parallel system and parallel-series system model from the three states of normal, short-circuit failure, and open-circuit Apr 17, 2018 · This paper presents a pioneering study on how to exploit deep learning for significant performance gain in wireless network optimization. We review this literature to organize seminal Mar 14, 2023 · Distribution network optimization has received a lot of attention in the literature. Jul 26, 2023 · Since its appearance, QML has shown promise in solving complex optimization problems such as NP-Hard problems, providing the potential to overcome the challenges faced in resource allocation, dynamic slicing, and traffic routing in 6G networks. Jan 1, 1981 · However, as we do wish to solve problems within a reasonable *Presented as a position paper on network optimization at the NSF Workshop on Operational Sciences, July 1979. (DRL) algorithms on network optimization, including network access, traffic control, offloading, and security Special Issue on "Optimization, Integration, and Future Directions in Low-Altitude Economy Networks" Submission deadline: 1 September 2025 The "low-altitude economy" comprises economic activities within the lower airspace layers, typically under 1,000 meters, where small drones, unmanned aerial vehicles (UAVs), and other aerial systems operate. The most common issues in IP network are unbalanced network workload and network attacks which can reduce the network performance. Main underlying technologies for the considered wireless networks are reviewed and discussed in section “Technologies”. In this paper, we first propose a Deep Q-Network (DQN) based network slicing algorithm to maximize the acceptance ratio and ensure prior placement of In this paper an optimized feedforward neural network model is proposed for detection of IoT based DDoS attacks by network traffic analysis aimed towards a specific target which could be constantly monitored by a tap. Submission deadline: Thursday, 31 October 2024 . In this work, we The hybrid optimization problem is formulated to provide a general theoretical framework for the analysis of a class of traffic control problems which takes into account the role of individual drivers as independent decisionmakers. There are three phases of optimization network reconfiguration such as start Mar 7, 2025 · We propose a new methodology for parameterized constrained robust optimization, an important class of optimization problems under uncertainty, based on learning with a self-supervised penalty-based loss function. We focus on the traveling salesman problem (TSP) and train a recurrent network that, given a set of city coordinates, predicts a distribution over different city permutations. M. Jan 29, 2025 · Optimization in Wireless Networks Antoine Oustry, Liding Xu, Sonia Haddad-Vanier, Juan-Antonio Cordero, Thomas Clausen 1 Introduction A wireless network is a network of electronic devices (computers, smartphones, sensors, actuators, or other connected objects) that communicate to each other, or to the Internet, through one or several wireless Jul 26, 2021 · In this paper, the optimization problem of public transit network is studied from the point of view of the reliability of public transit network. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. This e-book is for IT leaders who are ready to adopt a proactive approach to optimizing their networks and who want insights into the foundations necessary to prepare their networks for tomorrow. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an Dec 14, 2018 · Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). g. Since the problem of finding the optimum routing for any QoS metric is hard to solve for a medium or larger size network, quasi-optimization using metaheuristics, such as Genetic Algorithm (GA) and Simulated Annealing (SA), have been Dec 27, 2021 · Computer network topological structure is a kind of physical composition mode, which is composed of three aspects, namely, network computers or network equipment, nodes and lines in network transmission media. White papers & reports. This paper presentsEENet,anovelearly-exitingschedulingframework for multi-exit DNN models. Since the granularity of this data parallelization is largely-grained Jun 13, 2020 · Optimization is a critical component in deep learning. Data Manipulation Data Q uality One of the ways to find useful architectures for your tasks is to to read papers, blogs, Piazza (for this course) and understand the intricacies and measure relative performance for Aiming at this situation, this paper puts forward research on network planning in power system. He has written many research papers and he is the author or coauthor of thirteen textbooks and research monographs. Oct 4, 2022 · This paper studies network resource optimization in the 5G environment, proposes the particle swarm optimization (PSO) algorithm and uses the PSO algorithm to adjust each tenant’s bandwidth value to optimize network resource management. The paper further discusses the key challenges and future research directions in leveraging GAI for network optimization. Specifically, we summarize optimization techniques emerging from four general categories of commonly used network compression approaches, including network pruning, low-bit quantization, low-rank factorization, and Oct 1, 2021 · NetworkConnectivityOptimization: FundamentalLimitsandEffectiveAlgorithms ChenChen ArizonaStateUniversity chen_chen@asu. Figure 4. First, its tractability despite non-convexity is an intriguing question and may greatly expand our understanding of tractable problems. Finally, a case study demonstrates the application of a diffusion-based GAI model for load balancing, carrier aggre-gation, and backhauling optimization in non-terrestrial networks, a core technology of xG Mar 5, 2016 · He also lectures in network optimization and planning courses for computer networks. The neural network uses Adam optimization as a solver and the hyperbolic tangent activation function in all neurons from a In this paper, considering the characteristics of e-commerce logistics and the problems arising from the construction of logistics network, based on the full investigation and research of the B2C electricity supplier operation mode, we have studied and summarized the research results of scholars at home and abroad aiming at the related problems, and summarized and Feb 8, 2022 · The majority of existing approaches focus on learning data-driven optimizers that lead to fewer iterations in solving an optimization. In this research, we will conduct experiments using 7 of the most common optimization algorithms, namely: Stochastic Gradient Descent, Root Mean Jan 10, 2018 · Optimization problems are an important part of soft computing, and have been applied to different fields such as smart grids [1], logistics [2], [3], resources [4] or sensor networks [5]. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. yld vkhct qvnjxb xrhj ksfql kunsfy zkt qit qhx xsfcciy asjgm ogi tonyx nfkzr axrs