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Coloring Dynamic Graphs With a Similarity and Pool-Based Evolutionary Algorithm

IEEE ACCESS 2025年 13卷 38054-38075页

作者： Terci, Gizem Sungu Boz, Betul Gebze Tech Univ Inst Informat Technol TR-41400 Kocaeli Turkiye Marmara Univ Comp Engn Dept TR-34854 Istanbul Turkiye

The graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. Evolutionary algorithms, known for their adaptability and effectiveness in handling NP-hard problems, are well-suited for tackling the issues related to coloring dynamic graphs. In this paper, we present a novel Similarity and Pool-Based Evolutionary Algorithm designed to address the graph coloring problem on dynamic graphs. Our approach employs a partition-based representation that adapts to dynamic graph changes while preserving valuable historical information. The algorithm introduces an innovative similarity and conflict-based crossover operator aimed at minimizing the number of colors used, alongside a local search method to enhance solution diversity. We evaluated the performance of the proposed algorithm against a well-known heuristic for the graph coloring problem and a genetic algorithm with a dynamic population across a diverse set of dynamic graphs. Experimental results demonstrate that our algorithm consistently outperforms these alternatives by reducing the number of colors required in the majority of test cases.

关键词： Heuristic algorithms Color Evolutionary computation Social networking (online) Real-time systems partitioning algorithms Adaptation models Metaheuristics Genetic operators Communication networks Dynamic graphs edge-dynamic graphs node-dynamic graphs graph coloring problem crossover operator evolutionary algorithms

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Fast, Robust, and Learned Distribution-Based Sorting

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IEEE ACCESS 2025年 13卷 45198-45214页

作者： Ferragina, Paolo Odorisio, Mattia St Anna Sch Adv Studies Dept LEMbeDS Pisa Italy Univ Pisa Dept Comp Sci Pisa Italy

Despite a long history of research and achievements in designing sorting routines, new hardware features and application requirements pose advanced challenges that computer scientists are recently tackling through novel data-aware (learned) algorithms. This paper aims to better understand the strengths and limitations of learned sorters for numerical data, by addressing two main research and engineering questions: (Q1) Which is the best trade-off, in sorting items, between the efficacy of a "learned" model in approximating the distribution of the input data and its time-space complexity? and (Q2) Which algorithmic structure for distribution-based sorting is suited to leverage a learned model in order to achieve state-of-the-art performance? To answer Q1, we implement a variant of the best-known learned model (i.e., the two-layers RMI) and, also, design a fully new learned model that turns out to be space-time efficient and efficacious in approximating adversarial input distributions. We experimentally test them over 11 datasets of 200M and 800M 64-bit floating-point items, thus offering a comprehensive answer to Q1. We then address Q2 by plugging the best-learned models from above into a distribution-based sorting scheme that leads to three new sorters whose performance is tested over 33 datasets (real and synthetic) of sizes up to 800M items. Our experimental results will show that our sorters perform better than 6 (classic and learned) best-known sorters on 29 out of those 33 datasets, thus achieving new state-of-the-art tradeoffs.

关键词： Sorting Indexes Data models partitioning algorithms Approximation algorithms Synthetic data Proposals Hardware Numerical models Interpolation learned sort learned indexes learned-based data structures

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Enhanced Tube-Based Sampling for Accurate Network Distance Measurement with Minimal Sampling Scheduling Overhead

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第1期18卷 169-183页

作者： Tian, Jiazheng Wang, Cheng Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli Liang, Wei Hunan Univ Coll Comp Sci & Elect Engn Changsha 410012 Peoples R China Minist Educ Key Lab Fus Comp of Supercomp & Artificial Intelli Changsha 410002 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411199 Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing 100045 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China

The surge in demand for latency-sensitive services has propelled network distance measurement to the forefront of networking research. Utilizing the low-rank structure of full network data, the tensor completion method can efficiently estimate network distance from partially sampled distance data measured from a small set of node pairs. However, its performance is affected by sampling algorithm limitations, including unreliability and high overhead in dynamic networks. To tackle these challenges, we propose tube-based sampling as an alternative to point-based sampling, utilizing a partition-based algorithm to incorporate randomness for improved reliability. Additionally, we introduce a Tube Length Identification Algorithm to dynamically adjust tube length based on network status, balancing scheduling overhead reduction with estimation accuracy. Experimental results on three real network distance datasets, compared against 13 baseline algorithms, demonstrate the high accuracy and low scheduling overhead of our approach.

关键词： Tensors Accuracy Peer-to-peer computing Heuristic algorithms Estimation partitioning algorithms Distance measurement Matrix decomposition Fluctuations Position measurement Network distance measurement tensor completion tube based sampling

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Joint Dynamic Data and Model Parallelism for Distributed Training of DNNs Over Heterogeneous Infrastructure

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第2期36卷 150-167页

作者： Ling, Zhi Jiang, Xiaofeng Tan, Xiaobin He, Huasen Zhu, Shiyin Yang, Jian Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Inst Artificial Intelligence Hefei Comprehens Natl Sci Ctr Hefei 230026 Peoples R China New H3C Technol Co Ltd Beijing 100102 Peoples R China

Distributed training of deep neural networks (DNNs) suffers from efficiency declines in dynamic heterogeneous environments, due to the resource wastage brought by the straggler problem in data parallelism (DP) and pipeline bubbles in model parallelism (MP). Additionally, the limited resource availability requires a trade-off between training performance and long-term costs, particularly in online settings. To address these challenges, this article presents a novel online approach to maximize long-term training efficiency in heterogeneous environments through uneven data assignment and communication-aware model partitioning. A group-based hierarchical architecture combining DP and MP is developed to balance discrepant computation and communication capabilities, and offer a flexible parallel mechanism. In order to jointly optimize the performance and long-term cost of the online DL training process, we formulate this problem as a stochastic optimization with time-averaged constraints. By utilizing Lyapunov's stochastic network optimization theory, we decompose it into several instantaneous sub-optimizations, and devise an effective online solution to address them based on tentative searching and linear solving. We have implemented a prototype system and evaluated the effectiveness of our solution based on realistic experiments, reducing batch training time by up to 68.59% over state-of-the-art methods.

关键词： Training Data models Pipelines Optimization Synchronization Parallel processing Scalability partitioning algorithms Costs Computational modeling Distributed deep learning system data parallel model parallel data assignment model partitioning

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A Hybrid K-Means plus plus and Particle Swarm Optimization Approach for Enhanced Document Clustering

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IEEE ACCESS 2025年 13卷 48818-48840页

作者： Hassan, Eisha Malik, Fazila Khan, Qazi Waqas Ahmad, Nadeem Sardaraz, Muhammad Karim, Faten Khalid Elmannai, Hela Iqra Univ Islamabad Dept Comp & Technol Islamabad 44000 Pakistan Jeju Natl Univ Dept Comp Engn Jeju Si 63243 South Korea COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

Document Clustering has attracted the interest of many researchers who have created several solutions to this problem by combining different techniques, models, and algorithms. While famous for its simplicity, the most commonly used algorithm, K-Means, suffers from issues such as finding the optimal value for k and random initialization of the centroids. In this paper, we propose a hybrid methodology combining K-Means++ with the metaheuristic algorithm PSO to overcome the challenges of both these algorithms. K-Means++ is a smart initialization technique that selects clusters based on probability function so that they are further away from each other. Two methods, Elbow Analysis, and Silhouette Score, have been used to find the optimal value of k. The research also attempts to assess the results of the best-performing feature extraction techniques and create a combined approach using their average. Although the combined approach did not outperform the existing methods, it paved the way for further advancement and exploration of merging feature extraction techniques. The experiments were performed on four datasets: 20 Newsgroups, Reuters, WebKB, and Wine, and the results were evaluated using three evaluation metrics: Purity, Silhouette Score, and Jaccard Index. The proposed approach achieved a Purity of 0.95, 0.91. 0.86, 0.89, the Silhouette Score was measured to be 0.5, 0.05, 0.05, 0.59, and the Jaccard Index was reported to be 0.04, 0.04, 0.05, 0.14 on Reuters, WebKB, 20 Newsgroups, and Wine datasets, respectively

关键词： Clustering algorithms Accuracy Feature extraction Semantics partitioning algorithms Particle swarm optimization Organizations Indexes Space exploration Particle measurements Optimization algorithm feature extraction PSO text processing decision making

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A Computation Decomposition Strategy for Optimization-Based Coordination of Automated Vehicles in Confined Sites

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 558-568页

作者： Kojchev, Stefan Hult, Robert Kneissl, Maximilian Fredriksson, Jonas Volvo Autonomous Solut S-41873 Gothenburg Sweden Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden

Coordinating the motion profiles of fully automated vehicles in confined sites presents a challenge, particularly in avoiding conflicts in MUTually EXclusive (MUTEX) zones like intersections, merge-splits, and narrow roads. Prior methods have utilized optimization-based formulations, where the scheduling (order) of vehicles in MUTEX zones is found using a heuristic, followed by solving a nonlinear program (NLP) to determine the optimal state and control trajectories of each vehicle given the schedule. This paper presents a method that reduces the computational demand in the second step by decomposing the NLP into multiple smaller, parallelly solvable, NLPs. The method uses the confined area's road network geometry and the vehicle positions to identify non-significant MUTEX relations and leverage results from graph theory to use this information to identify independent subproblems. We demonstrate that the solution obtained using this approach is equivalent to that obtained by solving the original NLP. Furthermore, by utilizing the dual variables connected to the MUTEX enforcing constraints, the method is capable of a further, sub-optimality-inducing, subdivision of the problem, enabling a trade-off between optimality and computation. We show how the method can be utilized, both when the plan needs to be initially computed and when there is a need for updating an existing motion plan. Simulation examples demonstrate the computational improvement with respect to the non-decomposed problem.

关键词： Roads Trajectory Complexity theory Safety Optimal control Heuristic algorithms Graph theory Vehicle dynamics partitioning algorithms Intelligent transportation systems Autonomous vehicles cooperative systems graph theory motion control multi-agent systems optimization

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Gecko: Efficient Sliding Window Aggregation With Granular-Based Bulk Eviction Over Big Data Streams

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 698-709页

作者： Li, Jianjun Deng, Yuhui Huang, Jiande Yi, Zhou Yang, Qifen Min, Geyong Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China Columbus State Univ TSYS Sch Comp Sci Columbus GA 31907 USA Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF England

Sliding window aggregation, which extracts summaries from data streams, is a core operation in streaming analysis. Though existing sliding window algorithms that perform single eviction and insertion operations can achieve a worst-case time complexity of O(1) for in-order streams, real-world data streams often involve out-of-order data and exhibit burst data characteristics, which pose performance challenges to these sliding window algorithms. To address this challenging issue, we propose Gecko - a novel sliding window aggregation algorithm that supports bulk eviction. Gecko leverages a granular-based eviction strategy for various bulk sizes, enabling efficient bulk eviction while maintaining the performance close to that of in-order stream algorithms for single evictions. For large data bulks, Gecko performs coarse-grained eviction at the chunk level, followed by fine-grained eviction using leftward binary tree aggregation (LTA) as a complementary method. Moreover, Gecko partitions data based on chunks to prevent the impacts of out-of-order data on other chunks, thereby enabling efficient handling of out-of-order data streams. We conduct extensive experiments to evaluate the performance of Gecko. Experimental results demonstrate that Gecko exhibits superior performance over other solutions, which is consistent with theoretical expectations. In real-world data scenarios, Gecko improves the average throughput of the state-of-the-art algorithm b_FiBA by 1.7 times, with a maximum improvement of up to 3.5 times. Gecko also demonstrates the best latency performance among all compared schemes.

关键词： Streams Windows Out of order Binary trees Throughput Aggregates partitioning algorithms Time complexity Indexes Computer science Bulk eviction incremental computation out-of-order data streams sliding window aggregation stream algorithm

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Adaptive Task Assignment in Spatial Crowdsourcing: A Human-in-The-Loop Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2726-2739页

作者： Wu, Qingshun Li, Yafei Yan, Jinxing Zhang, Mei Xu, Jianliang Xu, Mingliang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

In recent years, adaptive task assignment has been explored in spatial crowdsourcing. The challenge lies in how to adaptively partition the task stream to achieve the best utility for task assignment. A number of existing works have attempted to solve this challenge and achieve better performance by utilizing learning-based methods. Specifically, they mainly employ reinforcement learning to divide the task stream into a series of suitable batches and then perform task assignment in a batch fashion. Drawing inspiration from the effectiveness of human-machine collaborative decision-making, we aim to investigate human-in-the-loop methods to further enhance the performance of adaptive task assignment. In this paper, we propose a novel framework called Human-in-the-Loop Adaptive Partition (HLAP), which consists of two primary modules: Reinforcement Learning Partition Decision (RL-PD) and Human Supervision and Guidance (HSG). In the RL-PD module, we develop an RL agent, referred to as the decision-maker, by integrating the dual attention network into the Deep Q-Network (DQN) algorithm to capture cross-dimensional contextual information and long-range dependencies for a better understanding of the environment. In the HSG module, we design a human-in-the-loop mechanism to optimize the performance of the decision-maker, focusing on addressing two key issues: when and how humans interact with the decision-maker. Furthermore, to alleviate the heavy workload on humans, we construct a supervisor based on RL to oversee the decision-maker's partition process and adaptively determine when human intervention is necessary. We conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency and effectiveness of the HLAP framework.

关键词： Human in the loop Crowdsourcing Roads Reinforcement learning Mobile computing Learning systems partitioning algorithms Costs Symbols Real-time systems Human-in-the-loop location-based services reinforcement learning spatial crowdsourcing task assignment

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Field-Based Security Testing of SDN Configuration Updates

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Malik, Jahanzaib Pastore, Fabrizio Univ Luxembourg SnT Ctr L-4365 Esch Sur Alzette Luxembourg

Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify;for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.

关键词： Clustering algorithms Testing Security Software Nearest neighbor methods Anomaly detection partitioning algorithms Inspection Software algorithms Machine learning algorithms Field-based testing software defined network (SDN) security testing upgrades testing

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FWCEC: An Enhanced Feature Weighting Method via Causal Effect for Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 685-697页

作者： Cao, Fuyuan Jing, Xuechun Yu, Kui Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Shanxi Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

Feature weighting aims to assign different weights to features based on their importance in machine learning tasks. In clustering tasks, the existing methods learn feature importance based on the clustering results derived from the collaborative contribution of all features, which overlooks the independent effect of each feature. In fact, there are underlying causal relationships between features and the clustering results, and the features with high causal effects are always more crucial for clustering. Therefore, we propose an enhanced Feature Weighting method via Causal Effect for Clustering, calculating the causal effect of each feature on the clustering results for obtaining the independent contribution of each feature. Specifically, we start by identifying the causal relationships among the features and utilizing the causal relationships to generate a reasonable treatment group. Next, we compare the changes in the data distribution between the treatment and control groups to determine the causal effect of each feature. Finally, the causal effects of features are used for enhancing the clustering-driven weight learning. Moreover, we present a theory of relative order consistency in causal effect. Experimental results demonstrate that utilizing causal effect in weight learning facilitates efficient convergence and achieves superior accuracy compared to state-of-the-art clustering algorithms.

关键词： Clustering algorithms Machine learning algorithms Machine learning partitioning algorithms Noise measurement Collaboration Linear programming Convergence Technological innovation Reviews Feature weights clustering causal effect feature importance

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