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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Reply to Comments on 'Direct-Splitting-Based CN-FDTD for Modeling 2D Material Nanostructure Problems'

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 676-678页

作者： Feng, Naixing Zhang, Yuxian Zeng, Qingsheng Tong, Mei Song Joines, William T. Wang, Guo Ping Institute of Microscale Optoelectronics Shenzhen University Shenzhen China College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Embedded System and Service Computing of the Ministry of Education College of Electronics and Information Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Duke University DurhamNC United States

In this response letter, we focus on addressing Yu Hang Yang's comments so that the correctness and validity of the proposed auxiliary-differential-equation-based (ADE) Crank-Nicolson finite-difference time-domain method with direct-splitting technique (CNDS-FDTD) are demonstrated. Moreover, related applications in modeling 2D material nanostructure problems are shown. © 2020 IEEE.

关键词： Finite difference time domain method

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Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

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Frontiers of computer Science 2020年第6期14卷 55-69页

作者： Qianchen YU Zhiwen YU Zhu WANG Xiaofeng WANG Yongzhi WANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China Shanxi Provincial Key Laboratory for Embedded System Xi'an 710072China College of Computer Science and Engineering North Minzu UniversityYinchuan 750021China College of Earth Exploration Science and Technology Jilin UniversityChangchun 130025China

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been ***,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined *** propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in *** Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been ***,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the *** this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout *** conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

关键词： graph convolutional neural network graph classification overlapping community detection nonparametric Bayesian generative model relational infinite latent feature model Indian buffet process uncollapsed Gibbs sampler posterior inference quality estimation

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Finite time cluster consensus of fractional-order multi-agent systems with directed topology

Finite time cluster consensus of fractional-order multi-agen...

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第33届中国控制与决策会议

作者： Weijin Liu Xiwei Liu Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education

In this paper,the finite time cluster consensus(FnTCC) of fractional-order multi-agent systems(FOMAS)with directed topology is *** fractional-order system is converted into an integer-order system by defining a neighborhood-based error variable,and suitable control rules are designed for the obtained first-order multi-agent *** to the exponential finite-time stability theorem,suitable Lyapunov functions are ***,the settling time function is *** simulation results prove the feasibility and validity of our theory.

关键词： Fractional-order Cluster consensus Exponential finite time Multi-agent systems Directed topology

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Mass flow rates effect on the performance of PV/T bi-fluid hybrid collector (single and simultaneous modes) 2nd

Mass flow rates effect on the performance of PV/T bi-fluid h...

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2nd International Conference on Electronic Engineering and Renewable Energy, ICEERE 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Modelization Information and Systems Laboratory Picardie Jules Verne University Amiens France DIEEI: Department of Electric Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789811562587

A hybrid photovoltaic/thermal collector (PV/T) is used to produce simultaneously electrical and thermal energy from absorbed solar irradiation. The research to date has tended to focus on either bi-fluids (water and air) as the working fluid to supply energy needs for different applications. The purpose of this work is to test the performances of PV/T at different operating modes of fluid e.g. the air mode, the water mode, and the simultaneous mode (water& air). Furthermore the effect of mass flow rate as a key parameter for better electrical and thermal performances has been investigated. The PV/T performances were assessed based on a dynamic numerical model. An energy balance equations have been established for each layer, then implemented in MATLAB software. The results show that thermal efficiency in the simultaneous mode (air & water) is better compared to others modes. The thermal efficiencies for independently fluid condition have ranged from approximately 20 to 48%, and increased to a maximum efficiency of near to 68% for the case of the simultaneously fluids. This result indicates that the optimum mass flow rates for air and water are 0.035 kg/s and 0.007 kg/s respectively. Therefore, the theoretical model developed of the independently and simultaneously operational modes is validated, evidencing a good fit between simulation results and the experimental data available in literature experimental. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Air

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Mobility-Aware User Association and Computation Offloading in Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Qiu, Bin Feng, Ke Li, Xian Xiao, Hailin Zhang, Zhongshan Guilin University of Technology School of Computer Science and Engineering Guilin541004 China Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541004 China Guangxi Modern Polytechnic College Hechi530700 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Wuhan430062 China School of Cyberspace Science and Technology Guangdong519088 China

The integration of mobile edge computing (MEC) into ultra-dense network (UDN) has emerged as a promising solution for providing uninterrupted task offloading services to computation-demanding mobile devices (MDs), leveraging densely deployed MEC servers. However, the mobility of MDs can significantly affect the computation offloading decision and system performance in UDN-MEC. To address this issue, we develop a joint optimization algorithm for mobility-aware user association and computation offloading to minimizize the total system cost, including delay and energy consumption in UDN-MEC. Specifically, a polynomial fitting algorithm based on an extended Kalman filter (PFA-EKF) is designed to predict the nonlinear moving trajectories of the MDs. Furthermore, we present a mobility-aware user-association rule based on a breadth-first search (UAR-BFS) that can realize balanced matching offloading in the overlapping coverage area of densely deployed small base stations. Accordingly, we propose an improved deep deterministic policy gradient-based greedy algorithm (IDDPG-GA) to address the joint optimization problem of multiuser dynamic offloading and resource allocation, in which an intelligently adjusted bias factor is proposed to enhance collaborative behavior among MDs. Extensive numerical results and comparisons demonstrate the promising performance improvements of the proposed scheme compared with existing benchmark schemes in various scenarios. © 2025 IEEE.

关键词： Computation offloading

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Virtual Source-Based Particle Swarm Optimizer for Multi-Source Location Using Mobile Swarm Robotics

Virtual Source-Based Particle Swarm Optimizer for Multi-Sour...

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第33届中国过程控制会议

作者： JunQi Zhang YuXuan Lin Qi Kang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service ComputingTongji UniversityMinistry of EducationShanghai Electronic Trans actions and Information Service Collaborative Innovation Center IEEE

Source location is an interesting topic in the field of swarm ***-source location is a vital branch of *** with a single-source location problem,the main difficulty for the multi-source location lies in that the number and distribution of sources are *** Swarm Optimizer(PSO) has been widely adopted for the single-source location ***,its performance deteriorates greatly when it faces multiple sources because it is designed to find the single global best solution,not multiple *** handle such a challenge,a virtual source mechanism is proposed to help PSO overcome this *** key idea of the virtual source mechanism is to divide the search area into multiple cells,each of which has a virtual *** virtual source leads the swarm to cover each cell such that particles can cover all cells in a search space before they *** results demonstrate that the virtual source mechanism-based PSO effectively locates multiple sources.

关键词： Particle swarm optimizer virtual source mobile swarm robotics multi-source location

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A Blockchain Based Automatic Access Scheme Design and Implement for Small Cell Base Station 21

A Blockchain Based Automatic Access Scheme Design and Implem...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Liang, Jingchao Wu, Jun Tan, Bin Ren, Haoqi Zhang, Zhifeng School of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Fudan University School of Computer Science Shanghai China Jinggangshan University Electronics and Information Engineering Ji'an China Tongji University Electronics and Information Engineering Shanghai China

ISBN: (纸本)9781665432061

5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station deployment to ensure signal coverage quality, which leads to more enormous construction costs. Therefore, it is urgent to find a safe and reliable solution that can use the existing public network to realize small cell base stations with automatic access. Based on this demand, we summarize various existing small cell base station automatic access technology solutions and their advantages and disadvantages while combing the characteristics of blockchain technology and its application solutions in similar scenarios. And then, we propose a new blockchain-based base station automatic access solution and implements the system in a practical scenario. In our solution, we innovatively introduce blockchain as an intermediate manager for small base stations and core networks, which solves traditional solutions requiring customized equipment while ensuring safety and reliability. It reduces costs and improves efficiency, but some problems are brought by the 'decentralization' of blockchain waiting to be solved. © 2021 IEEE.

关键词： Blockchain

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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Sensor Fusion of Camera and 2D LiDAR for Self-Driving Automobile in Obstacle Avoidance Scenarios 2

Sensor Fusion of Camera and 2D LiDAR for Self-Driving Automo...

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2nd International Workshop on Intelligent systems, IWIS 2022

作者： Nguyen, Tan-Thien-Nien Phan, Thanh-Danh Duong, Minh-Thien Nguyen, Chi-Tam Ly, Hong-Phong Le, My-Ha Hcmc University of Technology and Education Intelligent System Laboratory Ho Chi Minh City Viet Nam Soongsil University Department of Information and Telecommunication Engineering Seoul Korea Republic of University of Information Technology Computer Engineering Embedded Club Ho Chi Minh City Viet Nam Hcmc University of Technology and Education Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (数字)9798350398939

ISBN: (纸本)9798350398939

Obstacle dodging and overtaking are the pivotal tasks ensuring safety for self-driving automobiles. Multi-sensors fusion is the must-required condition to explore the entire surrounding information. This paper proposes a novel frontal dynamic car dodging strategy for automobiles with the left-hand side steering wheel by fusion of a camera and 2D LiDAR features. To begin with, we improve the LiteSeg model to extract the segmented mask, which can determine the drivable area and the avoiding direction. In addition to a camera, 2D LiDAR is used to check the scene information on the right side, which the camera's range cannot cover. As for point clouds output of 2D LiDAR, we adopt the Adaptive Breakpoint Detection (defined as ABD) algorithm to cluster the objects in a scanning plane. Subsequently, the RANSAC algorithm forms a straight line from the clustered point clouds to determine the boundary of the right-side obstacle. Besides, we compute the distance from LiDAR to the estimated straight line to maintain a safe distance when overtaking. Last but not least, the post-processing results of the two devices are fused to decide on the obstacle dodging and overtaking. The comprehensive experiments reveal that our self-driving automobile could perform well on the university campus in diverse scenarios. © 2022 IEEE.

关键词： Cameras

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