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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Pang, Lie Meng Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635054

To handle constrained multi-objective optimization problems (CMOPs), many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed. However, a recent study has shown that many of these CMOEAs do not perform well on real-world CMOPs. In contrast, NSGA-II, proposed over 20 years ago with a simple constrained dominance principle, has demonstrated better performance than recent CMOEAs on many real-world CMOPs. Motivated by NSGA-II’s promising results, this paper aims to further enhance its performance. We explore the idea of enhancing NSGA-II with a knee point for solving CMOPs. Specifically, a knee point is identified using the minimum distance from the estimated ideal point, and always included in the next population of NSGA-II. Experimental results show that this simple idea improves the performance of NSGA-II on real-world CMOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Trajectory planning and base attitude restoration of dual-arm free-floating space robot by enhanced bidirectional approach

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Frontiers of Mechanical Engineering 2022年第1期17卷 2-17页

作者： Zongwu XIE Xiaoyu ZHAO Zainan JIANG Haitao YANG Chongyang LI State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China Robotic System Department Jiangsu Jitri-Hust Intelligent Equipment TechnologyWuxi 214000China

When free-floating space robots perform space tasks,the satellite base attitude is disturbed by the dynamic *** disturbance of the base orientation may affect the communication between the space robot and the control center on *** this paper,the enhanced bidirectional approach is proposed to plan the manipulator trajectory and eliminate the final base attitude variation.A novel acceleration level state equation for the nonholonomic problem is proposed,and a new intermediate variable-based Lyapunov function is derived and solved for smooth joint trajectory and restorable base *** the method,the state equation is first proposed for dual-arm robots with and without end constraints,and the system stability is analyzed to obtain the system *** input modification further increases the system stability and simplifies the calculation *** are carried out in the end,and the proposed method is validated in minimizing final base attitude change and trajectory ***,the minute internal force during the coordinated operation and the considerable computing efficiency increases the feasibility of the method during space tasks.

关键词： free-floating space robot dual arm coordinated operation base attitude restoration bidirectional approach

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Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO Algorithms 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and NSGA-III have been still frequently used in application papers as shown by their number of citations. Since most EMO algorithms have been designed for and evaluated by artificial test problems such as DTLZ and WFG, they do not always work well on real-world problems. Main difficulties of real-world problems are as follows: (i) their Pareto front shapes are irregular, (ii) some objectives have much larger values than others, (iii) some decision variables have much larger values than others, (iv) some objectives are highly correlated, and (v) solution evaluation is expensive. In this paper, we focus on the first four difficulties (except for expensive solution evaluation). Our goal is to clearly explain problematic search behaviors of NSGA-II, MOEA/D, SMS-EMOA and NSGA-III caused by these difficulties using simple distance minimization problems. We formulate various distance minimization problems with the above-mentioned four difficulties by changing the location of each target point on the map as well as the size and shape of the map. We also suggest simple remedies for performance improvement of each algorithm for real-world applications without changing the algorithm itself. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

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Low-temperature metal–oxide thin-film transistor technologies for implementing flexible electronic circuits and systems

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Journal of Semiconductors 2023年第9期44卷 3-10页

作者： Runxiao Shi Tengteng Lei Zhihe Xia Man Wong State Key Laboratory of Advanced Displays and Optoelectronics and Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Here we review two 300℃metal–oxide(MO)thin-film transistor(TFT)technologies for the implementation of flexible electronic circuits and ***-enhanced TFTs for suppressing the variation and shift of turn-on voltage(VON),and dual-gate TFTs for acquiring sensor signals and modulating VON have been deployed to improve the robustness and performance of the systems in which they are *** circuit building blocks based on fluorinated TFTs have been designed,fabricated,and characterized,which demonstrate the utility of the proposed low-temperature TFT technologies for implementing flexible electronic *** construction and characterization of an analog front-end system for the acquisition of bio-potential signals and an active-matrix sensor array for the acquisition of tactile images have been reported recently.

关键词： flexible electronics metal-oxide semiconductor thin-film transistor dual gate fluorination analog front-end system sensors

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis intelligent prediction Improved CNN

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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