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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature 23

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Zou, Huiying Duan, Jia Liu, Xi Ren, Wei Li, Tao Zheng, Xianghan Choo, Kim-Kwang Raymond Hunan Engineering Research Center of Geographic Information Security and Application The Third Surveying and Mapping Institute of Hunan Province Hunan China China University of Geosciences School of Computer Science Wuhan China Sichuan University Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Casm State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Mnr Beijing China Fuzhou University College of Computer and Big Data China Sanya College School of Information and Intelligent Engineering China University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio United States

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach's security. © 2024 IEEE.

关键词： blockchain transaction cross-chain technology notary schemes proxy re-signature

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Hybrid Optimization Framework for Energy Efficiency Maximization in NOMA-Aided Internet of Vehicles with beyond Diagonal RIS

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IEEE Internet of Things Journal 2025年

作者： Yi, Xiaoxi Wang, Han Zhang, Chi Song, Huiling Dutta, Ashit Kumar Yichun University College of Physical Science and Technology Jiangxi Yichun336000 China City University of Macau Faculty of Data Science Macao999078 China Yichun University College of Physical Science and Engineering Yichun336000 China Harbin University of Commerce College of Basic Science Heilongjiang Harbin150006 China AlMaarefa University Ad Diriyah College of Applied Sciences Department of Computer Science and Information Systems Riyadh13713 Saudi Arabia

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with non-orthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for 6G vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association-a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as non-linear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: IoV association with BS, BS power allocation, and BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves

关键词： Reinforcement learning

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A motion-based compression algorithm for resource-constrained video camera traps

arXiv

引用

arXiv 2024年

作者： Ratnayake, Malika Nisal Gallon, Lex Toosi, Adel N. Dorin, Alan Agents and Decision-Making Group Dept. of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia Monash e-Research Centre Monash University ClaytonVIC3800 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3052 Australia Dept. of Software Systems and Cybersecurity Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is cheap with mass-produced hardware, storage, processing and transmission overheads provide a hurdle to acquisition of high resolution video from field-situated edge computing devices. Efficient compression algorithms are therefore essential if monitoring is to be conducted on single-board computers in situations where such hurdles must be overcome. Animal motion tracking in the field has unique characteristics that necessitate the use of novel video compression techniques, which may be underexplored or unsuitable in other contexts. In this article, we therefore introduce a new motion analysis-based video compression algorithm specifically designed for camera traps. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking on three popular edge computing platforms. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing overall data size by an average of 87% across diverse test datasets. Our experiments demonstrate the algorithm’s capability to preserve critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses. Our new software, EcoMotionZip, is available Open Access. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

arXiv

引用

arXiv 2021年

作者： Fiedler, Christian Scherer, Carsten W. Trimpe, Sebastian Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Germany Mathematical Systems Theory University of Stuttgart Germany Institute for Data Science in Mechanical Engineering RWTH Aachen University Germany

Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required. Although such rigorous bounds are available for Gaussian Processes, they are too conservative to be useful in applications. This often leads practitioners to replacing these bounds by heuristics, thus breaking all theoretical guarantees. To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. In particular, the bounds can be explicitly evaluated and are much less conservative than state of the art results. Furthermore, we show that certain model misspecifications lead to only graceful degradation. We demonstrate these advantages and the usefulness of our results for learning-based control with numerical examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Taiga Kamijo Kohei Aiso Kan Akatsu Yasuaki Aoyama Electrical Engineering and Computer Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan Department of Faculty of Engineering Division of Intelligent Systems Engineering Yokohama National University Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis.

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Transformer Model with Multi-Type Classification Decisions for Intrusion Attack Detection of Track Traffic and Vehicle

Transformer Model with Multi-Type Classification Decisions f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Quanlong Guan Tian Zhang Yu Qin Yuyu Zhou Yangguang Zhu Yuansheng Zhong Xiujie Huang Zhifei Duan Zhefu Li Changjiang Liu Xiaofeng Wu College of Information Science and Technology Jinan University Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China Guangdong Testing Institute of Product Quality Supervision Guangdong China Guangzhou XPeng Motors Technology Co. Ltd Guangdong China Guangzhou Polytechnic of Sports Guangzhou China

Security vulnerabilities, illustrated by the menace of track traffic or vehicle hacking, present a substantial risk to the Controller Area Network (CAN) bus, enabling unauthorized remote access and intrusion. Nevertheless, existing vehicle intrusion detection models encounter challenges in capturing temporal aspects, compromising the preservation of temporal attributes in the data. Addressing this predicament, we propose a novel Vehicle Intrusion Detection System (PTIDS) model based on Principal Component Analysis (PCA) and Transformer architecture, equipped with multi-type classification decisions. This model utilizes the PCA algorithm to preprocess the data and employs a multi-head self-attention mechanism to simulate the continuous input of the real-world environment in vehicle or track traffic data. Experimental results demonstrate that the PTIDS model outperforms traditional neural networks regarding accuracy, precision, recall, and F1-score for vehicle intrusion detection, confirming the importance of temporal variables in intrusion detection. However, owing to the high accuracy and low discrimination of the PTIDS model on the Car Hacking dataset, we migrated the model to the M-CAN and B-CAN intrusion datasets and conducted ablation experiments. The results reveal that the model achieved an accuracy of 90.14% on the M-CAN dataset, surpassing other models by 54.5%, demonstrating robust generalization ability.

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A Multi-tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Haiyang Xiao, Bo Zhou, Xiaokang Zhang, Yonghong Liu, Xiaodong the School of Software Nanjing University of Information Science and Technology Nanjing210044 China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China the School of Chemistry and Materials Science Nanjing University of Information Science and Technology Nanjing210044 China the Faculty of Business Data Science Kansai University Osaka565-0823 Japan the School of Automation Nanjing University of Information Science and Technology Nanjing210044 China the School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

—In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for computing power to be spread out. This lets interactive services respond quickly. Nevertheless, the computational resources at edge servers are inherently limited and often tasked with handling multiple concurrent operations. The inefficacious allocation of these resources significantly impairs the efficiency of task offloading. Additionally, indiscriminate offloading could overwhelm the servers, detrimentally impacting the performance of subsequent tasks. To circumvent these challenges, this study introduces a multi-tier offloading model predicated on game theory principles. This framework aims to optimize resource utilization at the edge while accounting for server load to ensure the timely execution of latency-sensitive tasks. To evaluate this model, this paper created a simulation environment specifically for video game tasks in consumer electronics. The experimental results show that the multi-tier offloading model can effectively relieve the load pressure on the edge server. The task failure rate of the multi-tier offloading model remains at the lowest level compared with several state-of-the-art algorithms, significantly reducing the execution delay of tasks and being able to meet the requirements of consumer electronics applications. © 2025 IEEE.

关键词： Computation offloading

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Wavelength-Division-Multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-Division-Multiplexed Transmission and Dispersion ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yuanbin Liu Hongyi Zhang Jiacheng Liu Liangjun Lu Jiangbing Du Yu Li Zuyuan He Jianping Chen Linjie Zhou Andrew W. Poon Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China Department of Electronic and Computer Engineering Photonic Device Laboratory The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s $\times 9$ on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and... 详细信息

ISBN: (纸本)9798350369311

关键词： Solitons Lasers and electrooptics Wavelength division multiplexing data communication Quantum cascade lasers Electro-optical waveguides Optical fiber dispersion Fiber lasers

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Hybrid Optimization of Horizontal Alignments in European Terrains: A Comparative Study 28th

Hybrid Optimization of Horizontal Alignments in European T...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Espeseth, Ane Juříček, Martin Ludwig, Harald Michael Tušar, Tea Center for Computing in Science Education University of Oslo Oslo Norway Faculty of Mechanical Engineering Brno University of Technology Brno Czech Republic Johannes Kepler University Linz Austria Department of Intelligent Systems Jožef Stefan Institute Ljubljana Slovenia Jožef Stefan Postgraduate School Ljubljana Slovenia

ISBN: (纸本)9783031900617

Path planning across terrain is a fundamental challenge in civil engineering, with applications ranging from transportation infrastructure to urban development. Recent advances in computational methods have enabled automated route optimization, particularly in horizontal alignment problems that balance construction costs with terrain constraints. However, standardized comparisons of optimization approaches across diverse geographical contexts remain limited, hindering the development of reliable automated planning systems. Here we show through a systematic comparative study across three European landscapes that A* significantly outperforms RRT* in initial path generation, with better computational efficiency and terrain adaptation, while PSO demonstrates superior optimization capabilities compared to CMA-ES and DE in refining these paths against roadway construction criteria. Through extensive parameter validation, we find these performance advantages remain consistent across different geographical contexts and topographical challenges, with the hybrid A*-PSO approach achieving significantly better results than applying optimization algorithms to straight-line paths alone. These findings provide a comprehensive comparison of key algorithms in infrastructure planning optimization, demonstrating the relative strengths of different approaches in horizontal alignment tasks. This comparative analysis offers practical guidance for algorithm selection while highlighting opportunities for further development through the incorporation of real-world engineering constraints. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Urban growth

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