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GBCT: Efficient and Adaptive Clustering via Granular-Ball Computing for Complex Data

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Xia, Shuyin Shi, Bolun Wang, Yifan Xie, Jiang Wang, Guoyin Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Key Lab Cyberspace Big Data Intelligent Secur Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Dept Comp Sci Chongqing 400065 Peoples R China

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in the human brain, resulting in those methodsbad performance in efficiency, generalization ability, and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering via granular-ball computing. First, clustering algorithm based on granular-ball (GBCT) generates a smaller number of granular-balls to represent the original data and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust;besides, as granular-balls can fit various complex data, GBCT performs much better in nonspherical datasets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also be used to improve other traditional methods.

关键词： Clustering algorithms partitioning algorithms Computational modeling Robustness Noise Approximation algorithms Mathematical models Clustering methods Telecommunications Shape Clustering granular computing granular-ball multigranularity

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Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization

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IEEE ACCESS 2025年 13卷 5129-5144页

作者： Yi, Yunfei Wang, Zhiyong Shi, Yunying Song, Zhengzhuo Zhao, Binbin Hechi Univ Sch Big Data & Comp Hechi 546300 Peoples R China Guangxi Normal Univ Coll Comp Sci & Engn GuiLin 541004 Peoples R China Guangxi Univ Sci & Technol Sch Automat Liuzhou 545616 Peoples R China

In recent years, the prevalence of Many-Objective Optimization Problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple Objective Particle Swarm Optimization (MOPSO), often face challenges of dimensionality and selection pressure when handling MaOPs. To overcome these challenges, this study proposes a Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization (CDA-MOPSO) algorithm. This algorithm introduces a convergence metric to assess the convergence status and solution distribution quality of the particle swarm during iterations. Based on this metric, Convergence-Aware Learning Factor Adjustment (CALFA), Convergence-Oriented Dimension Variation Strategy (CODVS), and Convergence-Driven Archive Maintenance (CDAM) operations are proposed. Additionally, evolutionary search is further conducted on the external archive to enhance algorithm performance. To validate the performance of the CDA-MOPSO algorithm, extensive experiments are conducted using standard test problems such as DTLZ and WFG. Experimental results demonstrate that the CDA-MOPSO algorithm exhibits superior convergence and solution distribution characteristics across multiple standard test functions, particularly in handling many-objective optimization problems, outperforming traditional multi-objective algorithms significantly. In conclusion, the CDA-MOPSO algorithm provides a novel solution for many-objective optimization problems, offering strong convergence capability and solution diversity, with broad prospects for practical applications.

关键词： Optimization Convergence Particle swarm optimization Maintenance Heuristic algorithms Vectors Search problems partitioning algorithms Measurement Clustering algorithms Many-objective optimization problems particle swarm optimization convergence

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CliReg: Clique-Based Robust Point Cloud Registration

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 1898-1917页

作者： Moratalla, Javier Laserna Carrillo, Pablo San Segundo Sanchez, David Alvarez Univ Politecn Madrid Ctr Automat & Robot CAR ETSIDI UPMCSIC Madrid 28012 Spain

We propose a branch-and-bound algorithm for robust rigid registration of two point clouds in the presence of a large number of outlier correspondences. For this purpose, we consider a maximum consensus formulation of the registration problem and reformulate it as a (large) maximal clique search in a correspondence graph, where a clique represents a complete rigid transformation. Specifically, we use a maximum clique algorithm to enumerate large maximal cliques and a fitness procedure that evaluates each clique by solving a least-squares optimization problem. The main advantages of our approach are 1) it is possible to exploit the cutting-edge optimization techniques employed by current exact maximum clique algorithms, such as partial maximum satisfiability-based bounds, branching by partitioning or the use of bitstrings, etc.;2) the correspondence graphs are expected to be sparse in real problems (confirmed empirically in our tests), and, consequently, the maximum clique problem is expected to be easy;3) it is possible to have a good control of suboptimality with a k-nearest neighbor analysis that determines the size of the correspondence graph as a function of $k$. The new algorithm is called CliReg and has been implemented in C++. To evaluate CliReg, we have carried out extensive tests both on synthetic and real public datasets. The results show that CliReg clearly dominates the state of the art (e.g., RANSAC, FGR, and TEASER++) in terms of robustness, with a running time comparable to TEASER++ and RANSAC. In addition, we have implemented a fast variant called CliRegMutual that performs similarly to the fastest heuristic FGR.

关键词： Point cloud compression Three-dimensional displays Robots Translation Accuracy Solid modeling Feature extraction Cost function Surveys partitioning algorithms Discrete optimization maximum clique mobile robotics point cloud 3-D registration scan matching

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Learning From Crowds by Class-Specific Instance Weighting

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Ji, Qiang Jiang, Liangxiao Zhang, Wenjun Li, Chaoqun China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Sch Math & Phys Wuhan 430074 Peoples R China

In crowdsourcing scenarios, we can acquire each instance's multiple noisy label set from crowd workers and then infer its integrated label via label integration. To further enhance the quality of integrated labels, some noise correction algorithms have been proposed in recent years. Most of them aim to partition the dataset into a clean set and a noise set, followed by training one or multiple classifiers on the clean set to rectify the instances in the noise set. However, they overlook the fact that the class distribution of the clean set is often inconsistent with that of the noise set, resulting in the subpar correction performance of trained classifiers. To mitigate this inconsistency, this paper proposes a class-specific instance weighting-based noise correction (CIWNC) algorithm. In CIWNC, each class's weight is computed based on the class distribution of the clean set firstly. Subsequently, a class-specific weight is computed for each instance using the weight of the class that its integrated label belongs to, as well as its multiple noisy label set. Finally, a classifier is trained on the instance weighted clean set to rectify the instances in the noise set. Experimental results on 34 simulated and two real-world datasets demonstrate that CIWNC outperforms existing state-of-the-art noise correction algorithms significantly.

关键词： Noise Classification algorithms Noise measurement Crowdsourcing Prediction algorithms Clustering algorithms Supervised learning Training Inference algorithms partitioning algorithms Crowdsourcing learning noise correction instance weighting class-specific instance weighting

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Partitioned Dynamic Hub Labeling for Large Road Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第5期37卷 2784-2801页

作者： Zhang, Mengxuan Zhou, Xinjie Li, Lei Zhou, Xiaofang Australian Natl Univ Sch Comp Canberra ACT 2601 Australia Hong Kong Univ Sci & Technol Clear Water Bay Hong Kong Peoples R China Hong Kong Univ Sci & Technol Thrust DSA Guangzhou 511455 Peoples R China Hong Kong Univ Sci & Technol Thrust INTR Guangzhou 511455 Peoples R China

Shortest path computation is ubiquitous in various applications in road networks and the index-based algorithms, especially hub labeling, can boost the query performance dramatically. However, traffic conditions keep changing in real life, making the precomputed index unable to answer the query correctly. In this work, we adopt the state-of-the-art tree decomposition-based hub labeling (TDHL) as the underlying index and design efficient algorithms to incrementally maintain the index. Specifically, we first analyze the structural stability of the index in dynamic road networks which enables us to concentrate on label value maintenance. We then introduce the minimum weight property and minimum distance property to guarantee index correctness without graph traversal. Moreover, we propose the star-centric paradigm for tracing index change and design various pruning techniques to further accelerate index maintenance. We also extend our algorithms to batch mode for shared computation, to structural maintenance for full types of updates, and generalize to all kinds of TDHL. Finally, we further improve the index maintenance efficiency and scalability of our algorithms by leveraging graph partition. Our experimental results validate the superiority of our proposals over existing solutions on both index maintenance and query processing.

关键词： Indexes Maintenance Roads Heuristic algorithms Labeling Query processing Scalability Costs Stars partitioning algorithms Dynamic shortest path index maintenance hub labeling

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ADPTD: Adaptive Data Partition With Unbiased Task Dispatching for Video Analytics at the Edge

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 7434-7445页

作者： Huang, Zhaowu Dong, Fang Zhu, Haopeng Liu, Mengyang Shen, Dian Zhou, Ruiting Guo, Xiaolin Chen, Baijun Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

Recently, edge-assisted methods have been proposed as a promising technique to deliver fast and accurate on-device video analytics by partitioning frame data and dispatching them to edge servers for parallel execution. However, the data partition (DP) reduces the detection latency but decreases accuracy since objects may cross the boundaries of adjacent blocks. The effect of DP on the accuracy and latency depends on multiple vital parameters (e.g., target size, density, network, and computing resources) in an unknown and time-varying fashion. Moreover, these parameters are determined by the application scenarios and edge environment, which are uncertain and heterogeneous at the edge. Hence, how to partition frames to strike a balance between accuracy and latency is a nontrivial and intractable problem. To this end, we propose an online learning-based device-edge-cloud collaboration framework, ADPTD, to guide DP at the edge. We propose an optimal task dispatching algorithm (OTD) to minimize detection latency. Then, we propose a multiarmed bandit-based algorithm to pick a DP strategy and invoke OTD to dispatch tasks in each time slot. Theoretical analysis reveals that ADPTD achieves sublinear regret. Extensive experimental results show that ADPTD outperforms the state-of-the-art methods, achieving a latency reduction of up to 2.53x and improving accuracy by up to 49.4%.

关键词： Accuracy Dispatching Visual analytics Image edge detection Servers partitioning algorithms Performance evaluation Monitoring Edge computing Low latency communication Data partition (DP) edge computing task dispatching (TD) video analytics

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Optimizing Clustered Cell-Free Networking for Sum Ergodic Capacity Maximization With Joint Processing Constraint

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 571-584页

作者： Xia, Funing Wang, Junyuan Dai, Lin Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Adv Study Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China City Univ Hong Kong Dept Elect Engn SAR Hong Kong Peoples R China

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking ((BCF)-F-2-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed (BCF)-F-2-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our (BCF)-F-2-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.

关键词： Clustering algorithms Wireless networks partitioning algorithms Interference Cellular networks Simulation Signal processing algorithms Uplink Optimization Computer architecture Clustered cell-free networking sum ergodic capacity maximization joint processing constraint bisection algorithm

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Assessment of K-Means Algorithm to Evaluate Nearshore Wave Climate

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IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第2期50卷 583-593页

作者： Castro, Elisa Iuppa, Claudio Musumeci, Rosaria Ester Xibilia, Maria Gabriella Patane, Luca Foti, Enrico Cavallaro, Luca Univ Catania Dept Civil Engn & Architecture I-95123 Catania Italy Univ Messina Dept Engn I-98166 Contrada Di Dio Italy

Accidents near ports have increased due to the ongoing expansion of maritime trade. These accidents have various causes, including adverse weather conditions. Accurate wave climate forecasts can help mitigate the risks associated with marine accidents. While numerical models are commonly used for obtaining nearshore wave climate forecasts, their high computational cost makes them impractical for wave climate forecasting and nowcasting. Artificial neural networks (ANNs) offer a potential solution to this limitation. However, existing ANNs have primarily focused on specific single points within the study areas, such as piers and port entrances. Enhancing early-warning strategies requires a broader understanding of the wave climate across larger areas. Thorough examinations of extensive areas with varying physical attributes can result in significant computational time requirements. The main objective of this study is to evaluate a clustering technique able to identify homogeneous areas to improve future applications of ANNs to assess nearshore wave characteristics in actual situations. The area around the port of Augusta (Sicily), one of the most important ports in Italy, serves as a case study in this article. Results show an optimal performance by applying the clustering algorithm K-means, capable of capturing the wave climate characteristics of the study area.

关键词： Meteorology Clustering algorithms Seaports Sea measurements Accidents Numerical models partitioning algorithms K-means maritime accidents SWAN wave climate

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A Robust FPGA Router With Optimization of High-Fanout Nets and Intra-CLB Connections

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第3期44卷 1003-1016页

作者： Jiang, Xun Wang, Jiarui Mai, Jing Di, Zhixiong Lin, Yibo Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Univ Elect Sci & Technol China Sch Integrated Circuits Sci & Engn Chengdu 610031 Peoples R China Peking Univ Beijing Adv Innovat Ctr Integrated Circuits Beijing 100871 Peoples R China Peking Univ Inst Elect Design Automat Wuxi 214000 Peoples R China

Routing is the most time-consuming step in the implementation flow of field programmable gate array (FPGA) designs. With the advance in transistor scaling and system integration, hardware resources in FPGA devices are growing in a larger quantity and diversity. The routing architecture is designed to be more complicated for mapping RTL designs to FPGA devices correctly, which brings significant challenges for current FPGA routing algorithms. The key challenges for routing algorithms lie in large solution space and heavy congestion, especially for high-fanout nets (HFNets). We propose a partition-based algorithm to accelerate the routing of HFNets, which decomposes the global routing guide to shrink the search space for connecting each sink. Meanwhile, the congestions existing inside configurable logic block (CLB) are hard to handle by traditional sequential negotiation-based algorithms, because the industrial routing architecture is quite complex. We propose a concurrent intra-CLB rerouting algorithm to effectively resolve routing congestion inside a CLB tile induced by connections between intra-CLB logic pins, e.g., logic elements and switch boxes. Experimental results on modified ISPD2016 benchmarks demonstrate that our framework can achieve 100% routability in 9.8% less wirelength and $11\times $ less runtime, while the state-of-the-art VTR 8.0 routing algorithm fails at 7 of 12 benchmarks.

关键词： Routing Field programmable gate arrays Computer architecture Pins Logic Video recording partitioning algorithms integer linear programming routing Steiner trees partitioning algorithms

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Coarse-Graining Complex Networks for Control Equivalence

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第2期70卷 1169-1175页

作者： Toller, Daniele Tribastone, Mirco Tschaikowski, Max Vandin, Andrea Aalborg Univ DK-9400 Aalborg Denmark IMT I-55100 Lucca Italy St Anna I-56127 Pisa Italy DTU Compute DK-2800 Lyngby Denmark

The ability to control complex networks is of crucial importance across a wide range of applications in natural and engineering sciences. However, issues of both theoretical and numerical nature introduce fundamental limitations to controlling large-scale networks. In this article, we cope with this problem by introducing a coarse-graining algorithm. It leads to an aggregated network which satisfies control equivalence, i.e., such that the optimal control values for the original network can be exactly recovered from those of the aggregated one. The algorithm is based on a partition refinement method originally devised for systems of ordinary differential equations, here extended and applied to linear dynamics on complex networks. Using a number of benchmarks from the literature we show considerable reductions across a variety of networks from biology, ecology, engineering, and social sciences.

关键词： Costs partitioning algorithms Cost function Vectors Trajectory Complex networks Time complexity Control systems costs linear systems mathematical models optimal control reduced order systems

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