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Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 719-733页

作者： Lin, Jianqing He, Cheng Tian, Ye Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

关键词： Optimization Computational modeling Iron Convergence Linear programming Predictive models Particle swarm optimization Kernel Costs clustering algorithms Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction

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Finding Rule-Interpretable Non-Negative Data Representation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第5期37卷 2538-2549页

作者： Mihelcic, Matej Miettinen, Pauli Univ Zagreb Dept Math Zagreb 10000 Croatia Univ Eastern Finland Kuopio 70210 Finland

Non-negative Matrix Factorization (NMF) is an intensively used technique for obtaining parts-based, lower dimensional and non-negative representation. Researchers in biology, medicine, pharmacy and other fields often prefer NMF over other dimensionality reduction approaches (such as PCA) because the non-negativity of the approach naturally fits the characteristics of the domain problem and its results are easier to analyze and understand. Despite these advantages, obtaining exact characterization and interpretation of the NMF's latent factors can still be difficult due to their numerical nature. Rule-based approaches, such as rule mining, conceptual clustering, subgroup discovery and redescription mining, are often considered more interpretable but lack lower-dimensional representation of the data. We present a version of the NMF approach that merges rule-based descriptions with advantages of part-based representation offered by the NMF. Given the numerical input data with non-negative entries and a set of rules with high entity coverage, the approach creates the lower-dimensional non-negative representation of the input data in such a way that its factors are described by the appropriate subset of the input rules. In addition to revealing important attributes for latent factors, their interaction and value ranges, this approach allows performing focused embedding potentially using multiple overlapping target labels.

关键词： Optimization Data mining Symmetric matrices Matrix decomposition Data visualization Data analysis Convergence Principal component analysis clustering algorithms Biomedical imaging Rules clustering subgroups redescriptions interpretable NMF guided embedding

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Underwater Target Tracking Based on Interrupted Software-Defined Multi-AUV Reinforcement Learning: A Multi-AUV Time-Saving MARL Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 2124-2136页

作者： Zhu, Shengchao Han, Guangjie Lin, Chuan Zhang, Yu Hohai Univ Coll Comp & Software Nanjing 210013 Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tech Minist Educ Nanjing 210013 Peoples R China Northeastern Univ Software Coll Shenyang 110819 Peoples R China

With the rapid development of underwater materials technology and underwater robot technology, human exploitation of marine resources has been increasingly advanced, which has given rise to various application scenarios for Autonomous Underwater Vehicle (AUV) cluster networks, such as cooperative data collection and target tracking. In this paper, we aim to explore how to utilize networking and swarm intelligence to improve the AUV cluster network's target tracking performance in a time-saving manner. Specifically, on account of our previous work, we introduce an underwater interrupted mechanism and propose an Interrupted Software-Defined Multi-AUV Reinforcement Learning (ISD-MARL) architecture. For MARL algorithm in ISD-MARL, we propose a time-saving MARL algorithm, S-MADDPG, integrating our proposed action optimization model and action network loss function, to accelerate the convergence of the MARL algorithm. Furthermore, to further improve the AUV cluster network's path planning performance during the target tracking, we propose an Interrupted Tracking Path Planning Scheme (ITPPS) for the AUV cluster network based on the proposed ISD-MARL and S-MADDPG. The evaluation results showcase that our proposed scheme can effectively plan the underwater target tracking path for the AUV cluster network in a shorter time and outperform various mainstream strategies in terms of convergence speed and training time, etc.

关键词： Target tracking clustering algorithms Path planning Collision avoidance Training Mobile computing Convergence Planning Computer architecture Underwater tracking Autonomous underwater vehicle underwater target tracking software-defined networking multi-agent reinforcement learning interrupted tracking path planning scheme

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Replanning-Oriented Framework for Efficient Real-Time Decision-Making in Multi-UAV Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第7期21卷 5127-5137页

作者： Hai, Xingshuo Tan, Longyan Feng, Qiang Duan, Haibin Wen, Changyun Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Cambridge Inst Mfg Dept Engn Cambridge CB3 0FS England Beihang Univ Natl Key Lab Reliabil & Environm Engn Beijing 100191 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

Efficient real-time decision-making for long-term multiple unmanned aerial vehicles (multi-UAV) missions in geo-distributed environments requires an integrated approach to manage dynamic task demands. We propose a hierarchical dual-layer decision-making framework for multi-UAV mission replanning. The upper layer optimizes multi-UAV deployment using the density-constrained K-medoids clustering and simulated annealing algorithm, achieving globally optimal solutions. The lower layer addresses task assignment via the goal-oriented belief space multiagent reinforcement learning algorithm, which leverages updated belief distributions to mitigate sparse reward and enhance training efficiency. Coordination between the two layers ensures comprehensive coverage of predefined demands while adapting to dynamic events. The effectiveness of the proposed methods is validated through a real-world case study using the 911 call dataset from city emergency services.

关键词： Heuristic algorithms Real-time systems Decision making clustering algorithms Autonomous aerial vehicles Training Drones Convergence Simulated annealing Resource management Decision-making multiagent reinforcement learning (MARL) multiple unmanned aerial vehicles (multi-UAV) replanning

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Multi-Graph Contrastive Learning for Community Detection in Multi-Layer Networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第3期9卷 2383-2397页

作者： Cao, Songen Lv, Xiaoyi Ma, Yaxiong Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xinjiang Univ Sch Software Urumqi 830046 Peoples R China

Multi-layer networks effectively describe and model complex systems in nature and society, with each layer corresponding to a different type of interaction relationship. Community detection in multi-layer networks aims to identify modules with strong connectivity in all layers, thus revealing interactions in complex systems. However, current algorithms have been criticized for failing to adequately quantify the underlying structure and extract discriminative features. To tackle these issues, we propose Multi-graph Contrastive Learning with Consistency and Diversity for clustering of multi-layer networks (MCLCD), which includes feature extraction, feature decomposition, contrastive learning, and feature fusion. Specifically, MCLCD utilizes joint nonnegative matrix decomposition to extract features from each layer of the network. To quantify the underlying structure, the features are decomposed into consistent and diverse parts. The former characterizes consistency in each layer, reflecting the community structure, while the latter models the specificity and noise in each layer. Then, MCLCD performs multi-graph contrastive learning with negative samples constructed from neighbor information, which enhances the discriminative properties of consistent parts. These parts are integrated into an objective function and an optimization process is derived. Extensive experiments on both artificial networks and real-world networks show that MCLCD surpasses state-of-the-art methods in terms of various metrics, highlighting the excellence of our proposed approach.

关键词： Feature extraction Contrastive learning Matrix decomposition Linear programming clustering algorithms Topology Social networking (online) Community structure consistency and diversity learning contrastive learning multi-layer networks nonnegative matrix factorization

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A Charging Scheme for Large-Scale Suburban WRSNs Based on the Collaboration of UAVs and Mobile PADs

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IEEE SENSORS JOURNAL 2025年第3期25卷 5615-5629页

作者： Chen, Yue Deng, Tingdan Tang, Shuqi Lin, Feng Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

Unmanned aerial vehicles (UAVs) are ideal mobile chargers (MCs) for energy-starved sensor nodes in wireless rechargeable sensor networks (WRSNs). Recently, the power access docker (PAD) is developed to extend the operating range of the UAV in large-scale WRSNs by recharging the UAV in the air. Existing research on WRSNs with PADs assumes that PADs have unlimited energy, implying that PADs are connected to consistent long-term energy supplies wherever they are deployed. However, this assumption may be achievable in urban WRSNs, but it is overly strict in suburban WRSNs, where only a few locations have reliable energy supplies. Consequently, the deployment of PADs in suburban WRSNs is constrained and cannot support UAV coverage of the entire surveillance area. Our work addresses this issue by introducing a vehicle-borne mobile PAD to large-scale suburban WRSNs. The mobile PAD may drive to places that the predeployed PADs cannot cover to recharge the UAV, allowing it to function across the whole area. To the best of our knowledge, this is the first work to consider the energy constraint of PAD deployment. To minimize the nodal charging delay, we investigate the problem of periodic charging with the collaboration of the UAV and the mobile PAD in the proposed new scenario. To address this problem, we propose the active rendezvous charging scheme (ARCS), which divides nodes into two types, plans the UAV's travel path, and schedules the rendezvous between the UAV and the mobile PAD. Simulation results show that ARCS can effectively address the energy problem in large-scale suburban WRSNs.

关键词： Autonomous aerial vehicles Sensors Batteries Energy conservation Charging stations Wireless sensor networks Heuristic algorithms Collaboration clustering algorithms Schedules Mobile PAD power access docker (PAD) periodic charging unmanned aerial vehicle (UAV) wireless rechargeable sensor network (WRSN)

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Enhanced 3D LiDAR Features TLG: Multi-Feature Fusion and LiDAR Inertial Odometry Applications

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1170-1177页

作者： Lu, Jiakai Liu, Jun Qin, Lan Li, Min Chongqing Univ Coll Optoelect Engn Minist Educ Key Lab Optoelect Technol & Syst Chongqing 400044 Peoples R China

In order to address the shortcomings of limited point cloud feature description capability, insufficient real-time performance, and severe feature homogenization in the field of 3D LiDAR SLAM. In this letter, a novel feature extraction and matching method TLG is proposed. First, the method makes full use of the geometric properties of point clouds to construct diversified keypoints by instantly capturing static block point clouds, obstacle edge points, and ground contour points. Secondly, utilize the distance and density thresholds of point cloud segmentation units to provide a binary high-speed fusion representation for the description of key points. Finally, implement feature matching based on the calculation of Hamming distance. This letter designs and implements a high-precision, robust LiDAR-inertial odometry framework named TLG-LIO, based on the proposed TLG features, and tests both the features and the framework using three public datasets. Experimental results show that the TLG features exhibit impressive matching accuracy and matching efficiency in the test results of multiple complex scenarios, with an average number of inliers and inlier% achieving 268.9 and 74.2%, and an average single-frame computation time of 0.0382 s. Meanwhile, the average single-agent Root Mean Square Error (RMSE) of TLG-LIO reaches 0.223 m, which is on par or even better than that of the classical odometry frame LIO-SAM performs comparably or even better.

关键词： Point cloud compression Feature extraction Laser radar Three-dimensional displays Odometry Accuracy Histograms Simultaneous localization and mapping Real-time systems clustering algorithms Mobile robot SLAM feature extraction binary descriptor 3D LiDAR inertial odometry

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De-Duplicated Hierarchical Offloading in Vehicular Edge Computing With Task Dependencies

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10293-10303页

作者： Liao, Zhuofan Shao, Zhenyi Zheng, Bin Tang, Xiaoyong Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China

In vehicular edge computing (VEC), most tasks require high real-time and energy requirements, but the mobility of vehicles and the difficulty of intelligent computing make it hard to meet these requirements. Due to the fact that most VEC tasks can be decomposed into smaller granularity, based on the dependencies between small subtasks, the repetition of tasks can be reduced, thereby improving task completion rates. In this work, we explore the dependencies of subtasks in different applications and design a two-stage multihop clustering de-duplication offloading (MCDO) mechanism. First, MCDO gives a multihop two-layer clustering (MTLC) algorithm to divide clusters based on similarities between different tasks. Based on this, MCDO further designs a de-duplication logical hierarchical offloading (DLHO). DLHO forms a directed acyclic graph (DAG) of de-duplicated subtasks in each cluster and offloads these subtasks in a logical hierarchical manner. Simulation results show that, compared to existing approaches PC5-GO, FedEdge, and MD-TSDQN, MCDO can achieve a minimum improvement of 15.1% in terms of latency and 20.8% in terms of energy consumption.

关键词： Computational modeling Roads Energy consumption Resource management Internet of Things Delays Cloud computing clustering algorithms Servers Edge computing Partial offloading resource allocation task dependency vehicle clusters vehicular edge computing (VEC)

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Tensorial Global-Local Graph Self-Representation for Hyperspectral Band Selection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 13213-13225页

作者： Zhang, Yongshan Qi, Jianwen Wang, Xinxin Cai, Zhihua Peng, Jiangtao Zhou, Yicong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa Macau Peoples R China Hubei Univ Fac Math & Stat Hubei Key Lab Appl Math Wuhan 430062 Peoples R China

Band selection aims at selecting a subset of representative bands from original hyperspectral images (HSIs) to alleviate data redundancy. There are at least two issues existing in previous methods. First, most of them ignore global or local structural information without considering both two aspects. Second, the high-order correlations among spectral bands are not explored during learning. In this paper, we propose a tensorial global-local graph self-representation (TGSR) method for hyperspectral band selection. Specifically, we segment the HSI into diverse superpixels to show the inherent spectral-spatial structures. Based on the generated superpixels, we learn the global and local graphs to explore complex structural information from global pixels and local regions. To alleviate the computational burden, a transformation is designed for easy graph convolution of global graph and pixel spectral matrix. With global and local knowledge, we formulate a global-local graph self-representation model to conduct band correlation learning in a self-weighted manner. To explore the high-order correlations among bands, we reorganize the self-representation coefficient matrices into a tensor with low-rank constraint. We design an alternating optimization algorithm to solve the proposed model. The most representative band is selected from each band subset by performing spectral clustering on the constructed affinity matrix. Experiments on HSI datasets verify the effectiveness of our method over the state-of-the-art methods. The source code is released at https://***/ZhangYongshan/TGSR.

关键词： Tensors Correlation Optimization Hyperspectral imaging Convolution clustering algorithms Adaptation models Hyperspectral band selection graph self-representation high-order tensor global-local information

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Assessment of the real-time pattern recognition capability of machine learning algorithms

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STATISTICAL ANALYSIS AND DATA MINING 2024年第3期17卷

作者： Polytarchos, Elias Bardaki, Cleopatra Pramatari, Katerina Athens Univ Econ & Business Dept Management Sci & Technol 28 Oktovriou 76 Athens Greece Harokopio Univ Dept Informat & Telemat Tavros Greece

Nowadays data streams from different sources, like blockchain-based and traditional financial transactions, social networks, and interconnected Internet of Things (IoT) devices, are becoming increasingly large in volume and the need to recognize patterns in real time from these streams, while adapting to their velocity and veracity, is emerging. Established machine learning algorithms used for pattern recognition methods have not been designed taking under account the volume, velocity, diversity, and accuracy of the data streams. This research contributes with an approach for assessing the pattern recognition capabilities of established machine learning algorithms when handling volatile data in real time and proposes a system that adapts the algorithms to the requirements of data streams, as well as assesses their pattern recognition capabilities based on established criteria. The system was applied for assessing five machine learning algorithms with input from a data stream from Bluetooth beacons tracking consumers in a retail store. This research can support future data scientists and analysts who need to reveal data patterns in big, volatile data streams in real time in order to support effective decision-making in the respective application domain. Copyright (c) 2024 John Wiley & Sons, Ltd.

关键词： clustering algorithms data streams IoT machine learning pattern recognition

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