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Research on the Route Planning Problem of Drone and Truck Collaborative Delivery in Restricted Areas

IEEE ACCESS 2025年 13卷 33062-33073页

作者： Zhou, Jun Yu, Qiang Xue, Ziming Yang, Leibo Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Aiming at the low efficiency of logistics distribution and the problem of restricted areas, we develop a collaborative distribution model that integrates trucks and drones, allowing for single takeoff and multiple drone deliveries. By considering the restricted areas for trucks and drones, a mathematical model has been constructed and a multi-stage heuristic optimization algorithm is proposed. This algorithm combines ant colony algorithm and simulated annealing algorithm, and a variety of neighborhood generation operators are introduced to improve the optimization effect. The model and algorithm are verified by different examples. Results show that the average solutions of algorithm is efficiency higher than Guorbi in small-scale instance, compared with the traditional truck distribution, the collaborative distribution shrink the 22.3% distribution time on average. Additionally, the increase in the restricted area customers and truck numbers has a significant impact on the complexity of route planning and distribution efficiency. Our research results can provide valuable guidance for future drone participation in complex urban and rural logistics distribution scenarios.

关键词： Drones Collaboration Heuristic algorithms Logistics Planning clustering algorithms Routing Traveling salesman problems Simulated annealing Resource management Area restriction drone delivery path planning logistics and transportation

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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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Deterministic Compression of Word Embeddings

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IEEE ACCESS 2025年 13卷 39248-39262页

作者： Nakamura, Yuki Suzuki, Jun Ito, Takumi Inui, Kentaro Tohoku Univ Grad Sch Informat Sci Sendai 9808579 Japan RIKEN Wako 3510198 Japan Langsmith Inc Tokyo 1706018 Japan

Word embeddings are an indispensable technology in the field of artificial intelligence, particularly when working with natural language processing models. To further enhance their usability, several studies have tackled the compression of word embeddings while maintaining task performance. In the same vein, this study proposes a word embedding compression method that vastly differs from recent neural compression methods. A notable characteristic of the proposed method is that it guarantees a stable and reproducible compressed representation under identical configurations. The central idea of the proposed method is utilizing a deterministic procedure and a convex optimization. More specifically, we leverage the property of obtaining identical solutions for identical convex optimization problems to construct a deterministic procedure throughout the entire compression process. We conduct experiments on both intrinsic and extrinsic evaluation with various word embeddings. Our experimental results show the effectiveness of the proposed method in terms of the performance vs. compression ratio trade-off and the ability to offer reproducible results. Notably, the results of the machine translation experiment on the extrinsic evaluation show that the proposed method compresses the word embedding layer of the Long Short-Term Memory (LSTM) Decoder by a factor of 258 with a 0.2 point decrease in BLEU Score.

关键词： Codes Vectors Memory management Vocabulary Dogs Principal component analysis Stability analysis Optimization Convex functions clustering algorithms Artificial intelligence natural language processing neural networks dimensionality reduction

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Hierarchical Multiscale Denoising Method for Spaceborne Photon-Counting LiDAR Based on Minimum Spanning Tree

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Dong, Zhipeng Wang, Yanli Liu, Yanxiong Feng, Yikai Li, Jie Chen, Yilan Tao, Junlin Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China Minist Nat Resources Key Lab Ocean Geomatics Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Nanjing 210023 Peoples R China

Spaceborne photon-counting light detection and ranging (LiDAR) can obtain high-precision bathymetry information for nearshore waters. However, the signals obtained by the spaceborne photon-counting LiDAR contain a large amount of noise due to atmospheric scattering, solar radiation, and instrumental noise. Therefore, the denoising of the spaceborne photon-counting LiDAR is a key component in the acquisition of bathymetric information. With respect to the difficulty of accurately denoising the density heterogeneity photons in different water depths, this article proposes a hierarchical multiscale denoising method for spaceborne photon-counting LiDAR based on minimum spanning tree. First, sea surface photons and water-column photons are obtained separately based on the kernel density estimation (KDE). Second, the minimum spanning tree is constructed and split based on the spatial distribution of water-column photons. In the process of splitting the minimum spanning tree, the length threshold of the interrupted branches is adaptively calculated. Then, water-column photons are denoised based on the hierarchical multiscale subtree node judgment strategy. Finally, water-column photon denoising results are refined using a box plot outlier detection (BPOD) method. The proposed method is compared with some state-of-the-art photon denoising algorithms using six trajectory data under various seafloor terrains. The experimental results demonstrate that the proposed method is better than other photon denoising algorithms, and can obtain accurately denoising results for different density distributions of photons under different seafloor terrains.

关键词： Photonics Noise reduction Bathymetry Sea surface Laser beams Laser radar clustering algorithms Noise Signal denoising Kernel Hierarchical multiscale photon denoising Land Elevation Satellite-2 (ICESat-2) kernel density estimation (KDE) minimum spanning tree spaceborne photon-counting LiDAR

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Efficient Hybrid-Robust Approach for Cancer Biomarker Discovery Using Omics Data

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IEEE ACCESS 2025年 13卷 51130-51149页

作者： Sid, Karima Zertal, Soumia Batouche, Mohamed Zerabi, Soumeya Oum El Bouaghi Univ Dept Math & Comp Sci Oum El Bouaghi 04000 Algeria Abdelhamid Mehri Constantine 2 Univ Lab Data Sci Comp & Artificial Intelligence LISIA Constantine 25016 Algeria Oum El Bouaghi Univ Dept Math & Comp Sci Lab Artificial Intelligence & Autonomous Things LI Oum El Bouaghi 04000 Algeria Princess Nourah bint Abdulrahman Univ Dept Informat Technol CCIS RC Riyadh 11671 Saudi Arabia Abdelhamid Mehri Constantine 2 Univ Dept Comp Sci & Its Applicat Lab Data Sci Comp & Artificial Intelligence LISIA Constantine 25016 Algeria

DNA Microarray datasets, also known as "omics" data, are important for the diagnosis of numerous diseases, including cancer and tumors. In the analysis of these data, feature selection techniques and classification algorithms are the workhorse for choosing candidate genes that serve as cancer biomarkers. However, microarray datasets present a challenge;they contain a greater number of features than the samples, which affects the performance of algorithms used in the analysis process. In order to extract precise information, it is necessary to employ a method that is both robust and performant. This paper emphasizes the importance of accurate and stable gene selection for the discovery of knowledge derived from high-dimensional data. A novel hybrid framework was put forth for consideration, comprising three distinct stages: clustering, Parallel Filtering, and Hybrid-Parallel Optimization. In each step, a combination of techniques and algorithms is used to improve the results in terms of stability and/or accuracy. The proposal is evaluated and tested according to different scenarios;using thirteen gene expression datasets and two classifiers: Artificial Neural Network (ANN) and Na & iuml;ve Bayes (NB). Comparison with related work demonstrates the efficacy of this approach, which enhances classification accuracy and stability while reducing the number of selected genes.

关键词： Feature extraction Stability criteria Classification algorithms Cancer clustering algorithms Gene expression Accuracy Approximation algorithms Metaheuristics Training Biomarker discovery microarray data feature selection classification optimization algorithms Markov blanket approximation

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Differentiation and Localization of Ground RF Transmitters Through RSSI Measures From a UAV

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1000-1009页

作者： Teeda, Vineeth Moro, Stefano Scazzoli, Davide Reggiani, Luca Magarini, Maurizio Politecn Milan I-20133 Milan Italy

Low-altitude Unmanned Aerial Vehicles (UAVs) are a valuable solution for data gathering, surveillance, warfare, and mapping. In these applications, differentiating and estimating the position of ground Radio Frequency (RF) emitters is pivotal. In order to achieve this, we define an experimental setup based on Received Signal Strength Indicator (RSSI) collected by a single UAV at different points of a predefined trajectory. The experimental setup is evaluated for the two unlicensed frequency bands of 2.4 GHz and 865 MHz with and without interference, respectively. We show that the application of the maximum likelihood algorithm to the RSSI measures collected in experiments conducted in rural areas gives a mean absolute localization error of about 5 m and 4 m for a single transmitter with and without interference, respectively. A threshold-based technique is proposed to improve the accuracy in the presence of interference. For multiple transmitters, the RSSI data are divided into clusters and fed into a localization algorithm. A k-means clustering algorithm eliminates user intervention and identifies the number of RF emitters in the area. As a further contribution of the paper, we performed a validation phase where the UAV flight path and data collection are simulated using the QuaDRiGa realistic radio impulse channel model.

关键词： Location awareness Radio frequency Autonomous aerial vehicles clustering algorithms Radio transmitters Interference Wireless sensor networks Unmanned aerial vehicles (UAVs) received signal strength indicator (RSSI) software defined radio (SDR) GNU radio localization interference k-means clustering

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StreamSoNGv2: Online Classification of Data Streams Using Growing Neural Gas

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 576-589页

作者： Dale, Jeffrey J. Keller, James M. Galusha, Aquila P. A. Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA

When dealing with unbounded streaming data, such as network packets or frames from a continuous live video feed, it is not feasible to apply iterative algorithms over the full dataset. The streaming soft neural gas (StreamSoNG) algorithm proposed by Wu et al. is particularly appealing given its ability to model arbitrary topologies in the data, however there are some shortcomings that make it difficult to apply in a practical setting. In this work, we propose and evaluate solutions to these shortcomings. Particularly, we offer an automated, data-driven approach to determining the value of a parameter $\eta$ to which the original algorithm is especially sensitive. We demonstrate that StreamSoNGv2 is competitive with related algorithms, shows improvement over the original, and that it provides useful soft labels for each streaming data point.

关键词： Prototypes Streams clustering algorithms Sociology Reservoirs Neurons Dogs Growing neural gas online classification streaming clustering streaming data analysis

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Global and Cluster Structural Balance via a Priority Strategy Based Memetic Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 874-888页

作者： Sun, Yifei Liu, Zhuo Jin, Yaochu Sun, Xin Cao, Yifei Yang, Jie Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710062 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China

Structural balance in signed networks aims to search for the structure with the least imbalance of relationships. However, most existing studies focus on global structural balance, and little work has considered local structural balance. In this study, an optimization model is proposed based on the weak definition of structural balance theory. The model incorporates both the global imbalance and maximum cluster imbalance as the criterion of structural balance in a signed network. Furthermore, a priority strategy based memetic algorithm, called PSMA, is presented to calculate the structural balance based on this model. In PSMA, a network-specific genetic operation is applied to explore the solution space. A multi-level greedy method is deployed to exploit the optimal solution as the local search. The priority strategy, which aims to recognize the severe unbalanced vertices or clusters as prior objectives and execute search operations on them, is inserted into each level of the local search to make it more efficient. Extensive experiments are conducted on 11 real-world network datasets. The results demonstrate that the proposed model can avoid clusters with over-concentration of unbalanced relationships at the expense of a slight increase in global imbalance and confirm that the proposed PSMA can achieve better global and cluster structural balance compared with the state-of-the-art algorithms.

关键词： clustering algorithms Biological cells Sociology Search problems Linear programming Computational modeling Sun clustering cluster structural balance multi-level priority strategy memetic algorithm signed networks

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FCER: A Federated Cloud-Edge Recommendation Framework With Cluster-Based Edge Selection

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 1731-1743页

作者： Wu, Jiang Yang, Yunchao Hu, Miao Zhou, Yipeng Wu, Di Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Macquarie Univ Fac Sci & Engn Dept Comp Sydney NSW 2112 Australia

The traditional recommendation system provides web services by modeling user behavior characteristics, which also faces the risk of leaking user privacy. To mitigate the rising concern on privacy leakage in recommender systems, federated learning (FL) based recommendation has received tremendous attention, which can preserve data privacy by conducting local model training on clients. However, devices (e.g., mobile phones) used by clients in a recommender system may have limited capacity for computation and communication, which can severely deteriorate FL training efficiency. Besides, offloading local training tasks to the cloud can lead to privacy leakage and excessive pressure to the cloud. To overcome this deficiency, we propose a novel federated cloud-edge recommendation framework, which is called FCER, by offloading local training tasks to powerful and trusted edge servers. The challenge of FCER lies in the heterogeneity of edge servers, which makes the parameter server (PS) deployed in the cloud face difficulty in judiciously selecting edge servers for model training. To address this challenge, we divide the FCER framework into two stages. In the first pre-training stage, edge servers expose their data statistical features protected by local differential privacy (LDP) to the PS so that edge servers can be grouped into clusters. In the second training stage, FCER activates a single cluster in each communication round, ensuring that edge servers with statistical homogenization are not repeatedly involved in FL. The PS only selects a certain number of edge servers with the highest data quality in each cluster for FL. Effective metrics are proposed to dynamically evaluate the data quality of each edge server. Convergence rate analysis is conducted to show the convergence of recommendation algorithms in FCER. We also perform extensive experiments to demonstrate that FCER remarkably out performs competitive baselines by 3.85%-9.14%on HR@10 and1.46%-11.77%on NDCG@10

关键词： Servers Training Privacy Recommender systems Data models Federated learning clustering algorithms Convergence Computational modeling Differential privacy recommender system privacy protections

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A Customized Genetic Algorithm for SLA-Aware Service Provisioning in Infrastructure-Less Vehicular Cloud Networks

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第2期18卷 499-512页

作者： Kaleibar, Farhoud Jafari St-Hilaire, Marc Barati, Masoud Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

Vehicular Ad-hoc Networks (VANETs) and in-vehicle networks offer complementary perspectives on Intelligent Transportation Systems (ITS), enabling communication between vehicles and within individual vehicles, respectively. While VANETs focus on vehicle-to-vehicle communication, the growing demand for dynamic resource sharing and data processing across a fleet of vehicles highlights the need for Vehicular Cloud Networks (VCNs). VCNs, despite their lack of fixed infrastructure and the continuous mobility of vehicles, provide a promising solution for improving resource management and data sharing, making them critical for achieving efficient Service Level Agreements (SLAs) in infrastructure-less environments. This article addresses these challenges by employing a hierarchical clustering technique and proposing a novel mathematical formulation for resource provisioning in infrastructure-less vehicular clouds. The formulation considers diverse criteria, including provider and requester mobility, data volume, and service delay tolerance, to ensure SLA adherence. A customized genetic algorithm is used to solve the maximization problem, incorporating a grouping mechanism for efficient problem solving. Simulations using the NS2 network simulator and the IBM CPLEX optimization tool validate the feasibility of the proposed approach and demonstrate its superior performance compared to the other methods.

关键词： Resource management Vehicle dynamics Genetic algorithms Reliability Optimization clustering algorithms Dynamic scheduling Quality of service Delays Transportation Vehicular network cloud services service level agreement (SLA) genetic algorithm

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