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DISTRIBUTED EXTENDED STOCHASTIC GRADIENT algorithm FOR JOINT IDENTIFICATION OF SYSTEM PARAMETERS AND NOISE MODEL PARAMETERS

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SIAM Journal on Control and Optimization 2025年第1期63卷 650-675页

作者： Gan, Die Yan, Ren Chen, Shuning Liu, Zhixin College of Artificial Intelligence Nankai University Tianjin China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China

In this paper, we study the distributed identification problem of dynamic systems described by stochastic regression models with colored noise. To deal with challenges brought by the colored noise, we provide estimates for the unknown noise over a previous time period, and incorporate them with observed signals to construct extended regression vectors. Based on this, we develop a distributed extended stochastic gradient algorithm to estimate unknown parameter matrices by integrating the diffusion strategy of extended regression vectors with the consensus strategy of neighbors' estimates. We introduce cooperative nonpersistent excitation conditions to reflect the temporal and spatial union information condition, under which the almost sure convergence of the proposed distributed algorithm is established. Our results are obtained without assuming the independence, stationarity or persistent excitation conditions for the regressors and the Gaussian property for the system noises. Finally, numerical results are provided to verify the effectiveness of our proposed algorithm. © by SIAM.

关键词： consensus algorithm

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Decision-making algorithm based on the energy of interval-valued hesitant fuzzy soft sets

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Neural Computing and Applications 2025年 1-21页

作者： Stojanović, Nenad Laković, Maja Djurović, Ljubica Faculty of Science University of Kragujevac 34000 Radoja Domanovića 12 Kragujevac Serbia

Interval-valued hesitant fuzzy soft sets, a powerful tool for data modeling, have been the focus of numerous studies. In addition to their various applications, examining the properties and characteristics of these structures from a theoretical mathematical perspective enables more efficient utilization in real-world problems. The primary objective of this research is to define a numerical characteristic of an interval-valued hesitant fuzzy soft set, analogous to the energy of a graph and the energy of a fuzzy soft set. The motivation for introducing this numerical characteristic lies in its potential to develop a decision-making algorithm that outperforms existing approaches addressing the same problem. The energy of a graph, as well as the energy of a fuzzy soft set, is defined as a norm function obtained by summing the singular values of the corresponding representation matrix. In optimization problems, this sum of singular values is known as the nuclear norm, which serves as the basis for introducing the notion of energy in interval-valued hesitant fuzzy soft sets. The results of the proposed algorithm are compared in this paper with those of various existing algorithms designed to solve the same problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： consensus algorithm

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Memetic algorithm for Maximizing K-coverage and K-Connectivity in Wireless Sensor Network

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 7-18页

作者： Thi, Hanh Nguyen Van Duc, Cuong Duc, Chinh Tran Minh, Hieu Ha Van, Son Nguyen Van, Quan La Faculty of Interdisciplinary Digital Technology PHENIKAA University Yen Nghia Ha Dong Hanoi12116 Viet Nam Hanoi University of Science and Technology Hanoi Viet Nam Faculty of Computer Science PHENIKAA University Yen Nghia Ha Dong Hanoi12116 Viet Nam

The rapid growth of IoT has enabled diverse applications using Wireless Sensor Networks across various fields. A significant challenge in Wireless Sensor Networks is the efficient deployment of sensors to ensure coverage and connectivity. Effective coverage allows continuous target tracking and data collection, while connectivity ensures data transmission to the base station. In this paper, we address the challenge of maximizing the number of targets satisfying K-coverage and K-connectivity, where each target is tracked by K sensors and has K transmission paths to the base station. We propose a two-phase methodology to tackle this challenge. The first phase enhances the Greedy algorithm to solve the K-coverage problem. The second phase addresses the K-connectivity problem using Memetic algorithms augmented by an efficient local search mechanism called PMA. We evaluate the algorithm on various datasets and compare it with baseline methods, including Greedy and Prim-based with the withdrawal strategy (PWS). Our results show that the proposed PMA with a robust local search outperforms alternative algorithms, with improvements exceeding 10% to 15% compared to the baseline methods. Additionally, we validate the performance of the proposed method using a real-world dataset and outline plans for further enhancements in the near future. © 2025 Slovene Society Informatika. All rights reserved.

关键词： consensus algorithm

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Practical Parallel algorithms for Non-Monotone Submodular Maximization

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Journal of Artificial Intelligence Research 2025年 82卷 39-75页

作者： Cui, Shuang Han, Kai Tang, Jing Li, Xueying Zhiyuli, Aakas Li, Hanxiao School of Computer Science and Technology Soochow University Jiangsu Suzhou China The Hong Kong University of Science and Technology China Alibaba Group Zhejiang Hangzhou China Qingdao University of Science and Technology Shandong Qingdao China

Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization. One measure of the parallelizability of a submodular maximization algorithm is its adaptive complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose a low-adaptivity algorithm achieving an (1/8 − ϵ)approximation with practical Õ(n) query complexity. Moreover, we also propose the first algorithm with both provable approximation ratio and sublinear adaptive complexity for the problem of non-monotone submodular maximization subject to a k-system constraint. As a by-product, we show that our two algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our algorithms is demonstrated by extensive experiments on real-world applications. ©2025 The Authors.

关键词： consensus algorithm

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An efficient collocation algorithm for third order non-linear Emden–Fowler equation

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Soft Computing 2025年第3期29卷 1767-1788页

作者： Alam, Mohammad Prawesh Khan, Arshad Department of Mathematics Jaypee Institute of Information Technology Noida62 201319 India Department of Mathematics Jamia Millia Islamia New Delhi110025 India

The study presents a novel algorithm for solving third-order non-linear equations (Emden–Fowler type), which can be applied to various physical models. The algorithm uses a quintic trigonometric B-spline collocation method and a quasilinearization technique to avoid the non-linearity term in the equation. The study established a comprehensive error analysis for the proposed algorithm and proved that it has fourth order, i.e., (O(h4)) convergent. The algorithm’s ability to handle singular behavior at the point x=0 and its faster rate of convergence exhibit a promising approach to solving such problems. The study also validates the theoretical results through numerical experiments and shows that the proposed algorithm has a faster rate of convergence in comparison to the existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： consensus algorithm

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Performance optimization of agricultural traceability blockchain based on sharding technology

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QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS 2025年第2期17卷 213-231页

作者： Guo, Xiangdong Zhang, Yuting Yao, Jingfa Teng, Guifa Hebei Agr Univ Coll Mechatron & Elect Engn Baoding 071001 Peoples R China Hebei Agr Univ Coll Informat Sci & Technol Baoding 071001 Peoples R China Hebei Software Inst Software Engn Dept Baoding 071000 Peoples R China Hebei Coll Intelligent Interconnect Equipment & Mu Big Data Applicat Technol Res Dev Ctr Baoding 071000 Peoples R China Hebei Key Lab Agr Big Data Baoding 071001 Peoples R China Hebei Digital Agr Ind Technol Res Inst Shijiazhuang 056400 Peoples R China

With the increasing global concerns over food safety, ensuring the transparency and traceability of agricultural products throughout their entire production-to-consumption process has become a key issue of societal interest. Blockchain technology, due to its decentralized, immutable, and transparent nature, offers a novel solution for the traceability of agricultural products. However, traditional blockchain systems encounter performance bottlenecks when handling large-scale agricultural data, particularly under conditions of high data volume and frequent transactions, where issues such as processing delays and insufficient throughput commonly arise. Sharding technology, a critical approach for improving blockchain performance, has the potential to significantly enhance system throughput and response time by partitioning the blockchain network into multiple independent shards for parallel processing. However, the application of sharding technology in agricultural traceability systems faces several challenges, including shard load imbalance, communication delays between shards, and inefficiencies in consensus algorithms. To address these challenges, two innovative approaches were proposed in this study: firstly, to address the issue of uneven load distribution in sharding, a jump hash sharding algorithm was designed to optimize shard allocation strategies, thereby improving resource utilization and processing efficiency;secondly, to tackle the performance bottleneck of consensus algorithms, an enhanced consensus algorithm was introduced to improve consensus efficiency while maintaining security. Experimental results demonstrated that the proposed method significantly outperformed traditional blockchain-based traceability systems in terms of performance and scalability, providing a more efficient and reliable technological solution for the application of blockchain technology in the agricultural sector. This research not only offers a more efficient technological solu

关键词： agricultural product traceability blockchain consensus algorithm hump hash sharding algorithm performance optimization sharding technology

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Developing Novel algorithms for Generating Inexact Data Through Triangle Distribution

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IEEE Transactions on Big Data 2025年第3期11卷 1411-1418页

作者： Aslam, Muhammad King Abdulaziz University Department of Statistics Faculty of Science Jeddah21551 Saudi Arabia

The manuscript introduces the mathematical representation of the neutrosophic triangular distribution, encompassing probability density functions and cumulative distribution functions. Two algorithms are introduced for the generation of random variates based on this distribution. Through simulations and a comparative examination with traditional statistical approaches, the research illustrates the versatility and resilience of the neutrosophic triangular distribution. The findings underscore its effectiveness in addressing uncertainty, particularly in scenarios with varying degrees of indeterminacy. The study emphasizes the substantial influence of uncertainty on the generation of random variates, with potential implications for decision-making and data analysis. © 2015 IEEE.

关键词： consensus algorithm

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An Enhanced Semisteady-State Jaya algorithm With a Control Coefficient and a Self-Adaptive Multipopulation Strategy

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JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2025年第1期2025卷

作者： Ankrah, Joshua Churchill Effah, Francis Boafo Twumasi, Elvis Kwame Nkrumah Univ Sci & Technol Elect Engn Dept Kumasi Ghana

This paper introduces the enhanced semisteady-state Jaya (ESJaya) algorithm, an improved version of the semisteady-state Jaya (SJaya) algorithm. The ESJaya algorithm uses a control coefficient to regulate the influence of the best solution, achieving a better balance between exploration and exploitation. It also features a self-adaptive multipopulation strategy and removes absolute value operators in its update equation for improved performance. Using 20 benchmark functions, the ESJaya algorithm is compared with the traditional Jaya, SJaya, and elitism-based self-adaptive multipopulation Jaya (SAMPEJaya) algorithms. The ESJaya algorithm ranks first in obtaining optimum values, mean accuracy, and stability, showing better overall performance than the others. The ESJaya algorithm ranks first with an average overall rank of 1.750, followed by Jaya, SAMPEJaya, and SJaya with average overall ranks of 2.650, 2.700, and 2.900, respectively. The Wilcoxon signed-rank test confirms the statistical significance of the overall rankings. When compared with the Snow Geese algorithm (SGA), Big Bang-Big Crunch algorithm (BBBCA), firefly algorithm (FA), bat algorithm (BA), cuckoo search algorithm (CSA), and flower pollination algorithm (FPA), the ESJaya algorithm gives competitive results in terms of obtaining optimum values, mean accuracy, and stability. Using four real-world engineering problems, the ESJaya algorithm is compared with the recently proposed piranha predation optimization algorithm (PPOA) and four state-of-the-art algorithms, namely, the COLSHADE algorithm, the sCMAgES algorithm, the SASS algorithm, and the EnMODE algorithm. The ESJaya algorithm finds optimum solutions with good accuracy, consistency, and stability. Overall, it has a slower convergence and longer run time due to its multipopulation strategy and its search for the best solution after each solution update. However, it consistently finds more optimal solutions than other improved algorithms.

关键词： consensus algorithm

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Edge connectivity of a neutrosophic graph

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Neutrosophic Sets and Systems 2025年 81卷 729-740页

作者： Tripathy, Aparna Panada, Amaresh Chandra Behera, Siva Prasad Mohanty, Bhabani S. Department of Mathematics C.V. Raman Global University Odisha Bhubaneswar752054 India Department of Statistics and Applied Mathematics Central University of Tamil Nadu India

Neutosophic graphs are an extension of fuzzy and intuitionistic fuzzy graphs by including the uncertainty, vagueness, and indeterminacy that are normal in the real world. This paper looks into the edge connectivity of a neutrosophic graph, which is a basic parameter that shows how strong and fault-tolerant networks are that are modelled by these graphs. Edge connectivity, which is the smallest number of edges that need to be taken away from a graph to make it trivial or disconnected, is a key concept in figuring out how strong and resilient networks are in many situations. This paper also addresses computational challenges related to determining edge connectivity in neutrosophic graphs. We develop efficient algorithms that minimize computational overhead and ensure accuracy in identifying the critical edge sets. We analyze the performance of these algorithms through both theoretical complexity assessments and empirical evaluations on benchmark datasets. Some of the most important things that the study found were critical edges that, when removed, have a big effect on how connected the graph is and how indeterminacy affects the strength of networks. The research underscores the importance of incorporating neutrosophic parameters into graph connectivity studies to better model and analyse systems characterized by uncertainty and partial knowledge. © 2025, University of New Mexico. All rights reserved.

关键词： consensus algorithm

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Collaborative Intrusion Detection Approach Based on Blockchain in Internet of Vehicles

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IEEE INTERNET OF THINGS JOURNAL 2025年第9期12卷 11965-11976页

作者： Xing, Rui Su, Zhou Wang, Yuntao Jingdezhen Ceram Univ Sch Informat Engn Jingdezhen 333403 Jiangxi Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710049 Shaanxi Peoples R China

As the Internet of Vehicles (IoV) advances, the security concerns surrounding vehicular networks have grown increasingly critical due to the openness of networking among vehicles, inadvertently creating more opportunities for adversaries to infiltrate and potentially disrupt vehicle operations. Intrusion detection systems (IDSs) stand as a promising solution, effectively mitigating the myriad of threats and security concerns that plague vehicles. In this article, we delve into the realm of IDSs within vehicular networks and propose an innovative collaborative intrusion detection framework based on blockchain technology and auction game. First, we integrate a vehicular blockchain into the IDS, offering a holistic approach to tackling both internal and external threats within vehicular networks. Second, we introduce a novel assistant-delegated Byzantine fault tolerance (A-DBFT) consensus algorithm, designed to bolster the efficiency of intrusion detection within the blockchain while maintaining the efficacy of the consensus mechanism. Third, we develop an auction game mechanism that incentivizes assistants and verifiers to actively initiate and participate in auctions, thereby enhancing the overall security of our intrusion detection scheme. Ultimately, we present simulation results that validate the superiority of our proposed scheme compared to conventional approaches.

关键词： Blockchains Intrusion detection Security Games Collaboration consensus algorithm Privacy Fault tolerant systems Fault tolerance Internet of Vehicles Assistant-delegated Byzantine fault tolerance (A-DBFT) blockchain Internet of Vehicles (IoV) intrusion detection

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