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Efficient surrogate-based optimization method and application based on collaborative improvement aggregation strategy

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Kongzhi yu Juece/control and Decision 2025年第6期40卷 1975-1984页

作者： Xiao, Tian-Li Wu, Feng Lin, Cheng-Long School of Management Science and Engineering Anhui University of Technology Ma’anshan243002 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan243002 China School of Economics and Management Anhui Polytechnic University Wuhu241000 China School of Economics and Management Nanjing University of Science and Technology Nanjing210094 China

To address the problem that greedy characteristics lead to slow convergence and low accuracy problems of surrogate-based optimization solutions in the later stage, a collaborative improvement aggregation strategy is proposed, which is extended to an efficient surrogate-based optimization method for expensive black-box problems. The proposed strategy integrates probability improvement and mean improvement criteria using Chebyshev decomposition and achieves a balance between global exploration and local search capabilities through random weight coefficients. In addition, the differences between surrogate-based optimization and surrogate-assisted optimization methods are analyzed from the perspective of candidate point sets, further exploring the role of randomness in optimization design. The experimental results show that this method can effectively improve the convergence accuracy of the optimized solution in black-box problems. Compared with similar techniques, the proposed method has higher convergence accuracy and stronger robustness of optimized solutions. © 2025 Northeast University. All rights reserved.

关键词： Tabu search

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Hongjian Zeng Yonghua Xiong Jinhua She Anjun Yu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Operation Management Department Jiangxi Ganyue Expressway Company Ltd. Nanchang Jiangxi China

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms.

关键词： Sensors Costs Time factors Data collection Crowdsensing Evolutionary computation Data integrity Optimization Inference algorithms Mobile computing

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Research on Temperature Error Calibration Method of Fluxgate Sensor

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Journal of Advanced Computational Intelligence and Intelligent Informatics 2025年第3期29卷 583-591页

作者： Yang, Ruiping Ge, Jian Luo, Wang Hu, Xiangyun She, Jinhua Chugo, Daisuke Dong, Haobin School of Automation China University of Geosciences No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education No 388 Lumo Road Hongshan District Hubei Wuhan430074 China School of Engineering Kwansai Gakuin University 1 Gakuen Uegahara Hyogo Sanda669-1330 Japan School of Geophysics and Geomatics China University of Geosciences No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Hubei Subsurface Multi-scale Imaging Key Laboratory No.388 Lumo Road Hongshan District Hubei Wuhan430074 China School of Engineering Tokyo University of Technology 1404-1 Katakuramachi Hachioji Tokyo192-0982 Japan

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. Compared with other temperature calibration methods, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE = 11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background. © Fuji Technology Press Ltd.

关键词： Magnetic field measurement

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Payoff-based accumulative effect promotes cooperation in spatial prisoner’s dilemma

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Chinese Physics B 2010年第9期19卷 53-58页

作者：刘永奎李智陈小杰王龙 Center for Complex Systems Department of Automatic Control EngineeringXidian University State Key Laboratory for Turbulence and Complex Systems Center for Systems and ControlCollege of EngineeringPeking University

We study the effect of accumulative payoff on the evolution of cooperation in the evolutionary prisoner＇s dilemma on a square lattice. We introduce a decaying factor for the accumulative payoff, which characterizes the extent that the historical payoff is accumulated. It is shown that for fixed values of the temptation to defect, the density of cooperators increases with the value of the decaying factor. This indicates that the more the historical payoff is involved, the more favourable cooperators become. In the critical region where the cooperator density converges to zero, cooperators vanish according to a power-law-like behaviour. The associated exponents agree approximately with the two-dimensional directed percolation and depend weakly on the value of the decaying factor.

关键词： payoff accumulative effect prisoner＇s dilemma cooperation

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Evolutionary prisoner's dilemma game on highly clustered community networks

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Chinese Physics B 2009年第7期18卷 2623-2628页

作者：刘永奎李智陈小杰王龙 Center for Complex Systems Department of Automatic Control Engineering Xidian University State Key Laboratory for Turbulence and Complex Systems Center for Systems and Control College of Engineering Peking University

This paper studies the evolutionary prisoner＇s dilemma game on a highly clustered community network in which the clustering coefficient and the community size can be tuned. It finds that the clustering coefficient in such a degree-homogeneous network inhibits the emergence of cooperation for the entire range of the payoff parameter. Moreover, it finds that the community size can also have a marked influence on the evolution of cooperation, with a larger community size leading to not only a lower cooperation level but also a smaller threshold of the payoff parameter above which cooperators become extinct.

关键词： prisoner＇s dilemma cooperation clustering coefficient community structure

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Integrated Communication and Learned Recognizer with Customized RIS Phases and Sensing Durations

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IEEE Transactions on Communications 2025年

作者： Huang, Yixuan Yang, Jie Wen, Chao-Kai Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Southeast University Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Nanjing210096 China

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed at https://***/kiwi1944/CRISense. © 1972-2012 IEEE.

关键词： Long short-term memory

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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Integrated Communication and Learned Recognizer with Customized RIS Phases and Sensing Durations

arXiv

引用

arXiv 2025年

作者： Huang, Yixuan Yang, Jie Wen, Chao-Kai Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan

关键词： Long short-term memory

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Cooperative Resource Allocation for NOMA-MEC Multi-Cell Network

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IEEE Transactions on Vehicular Technology 2025年

作者： Cai, Yujin Zhang, Zaichen Huang, Yongming Yu, Wenwu Nie, Xiaokai Liu, Hongzhe Southeast University School of Mathematics Jiangsu Nanjing211189 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

Integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) enables IoT nodes to offload their complicated tasks to MEC servers. Most of the existing resource allocation schemes in NOMA-MEC networks are based on the centralized optimization framework, which may increase the communicational and computational burden on the central unit. In this paper, the NOMA-MEC multi-cell network without the central unit is considered. To minimize the task completion time and offloading cost, a joint optimization problem including sub-channel allocation, transmit power, offloaded proportions, computing frequencies of IoT nodes and MEC servers, and price of MEC resources is investigated. In order to tackle the formulated mixed integer nonlinear programming problem, a two-stage low-complexity distributed algorithm based on the cooperation of all base stations (BSs) is proposed. Specifically, in Stage 1, a distributed heuristic sub-channel allocation algorithm is designed based on the sub-channel gains. In Stage 2, with the obtained sub-channel allocation result, a cooperative distributed continuous variable optimization algorithm is devised based on the distributed optimization and alternating optimization methods. Numerical results demonstrate the convergence of the proposed two-stage algorithm for three interference cases. Besides, the impacts of system parameters and schemes on the network performance are analyzed. More importantly, the resource allocation time can be significantly shortened by employing the cooperation of BSs compared with the centralized algorithm © 1967-2012 IEEE.

关键词： Nonlinear programming

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