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Solving Multiobjective Combinatorial Optimization via Learning to Improve Method

IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第3期9卷 2122-2136页

作者： Ye, Te Zhang, Zizhen Zhang, Qingfu Chen, Jinbiao Wang, Jiahai Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510275 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong

Recently, neural combinatorial optimization (NCO) methods have been prevailing for solving multiobjective combinatorial optimization problems (MOCOPs). Most NCO methods are based on the "Learning to Construct"(L2C) paradigm, where the trained model(s) can directly generate a set of approximate Pareto optimal solutions. However, these methods still suffer from insufficient proximity and poor diversity towards the true Pareto front. In this paper, following the "Learning to Improve"(L2I) paradigm, we propose weight-related policy network (WRPN), a learning-based improvement method for solving MOCOPs. WRPN is incorporated into multiobjective evolutionary algorithm (MOEA) frameworks to effectively guide the search direction. A shared baseline for proximal policy optimization is presented to reduce variance in model training. A quality enhancement mechanism is designed to further refine the Pareto set during model inference. Computational experiments conducted on two classic MOCOPs, i.e., multiobjective traveling salesman problem and multiobjective vehicle routing problem, indicate that our method achieves remarkable results. Notably, our WRPN module can be easily integrated into various MOEA frameworks such as NSGA-II, MOEA/D and MOGLS, providing versatility and applicability across different problem domains. © 2025 IEEE.

关键词： Deep reinforcement learning

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E-Commerce Personalized Recommendation System in big data Based on Three-Way Concept Lattices 3

E-Commerce Personalized Recommendation System in Big Data Ba...

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3rd International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Xu, Hongsheng Gu, Jing College of Electronic and Commerce Luoyang Normal University Luoyang China Luoyang Normal University Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang China

ISBN: (纸本)9798350376173

The e-commerce recommendation system can automatically judge the preferences of users according to the browsing situation, and recommend relevant products to customers based on this basis to reduce the time wasted by customers in looking for products. The e-commerce recommendation system is to use the e-commerce big data platform to provide consumers with product information and relevant suggestions, to recommend consumers to buy their favorite types of products, and to imitate the salesman to help consumers to complete the purchase behavior. The paper presents e-commerce personalized recommendation system in big data based on three-way concept lattices. Finally, the method experimentally proves that the proposed method achieves accurate personalized recommendation results and showing better prediction accuracy, which has strong scalability. © 2024 IEEE.

关键词： Marketplaces

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Placement of Deep Reinforcement Learning in Service Function Chain of Network Function Virtualization 2

Placement of Deep Reinforcement Learning in Service Function...

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2nd International Conference on data Science and Information System, ICDSIS 2024

作者： Xu, Hongsheng Huang, Kaifeng Luoyang Normal University College Of Electronic And Commerce Luoyang China Luoyang Normal University Henan Key Laboratory For Big Data Processing & Analytics Of Electronic Commerce Luoyang China

ISBN: (纸本)9798350381665

Reinforcement learning is a goal-oriented algorithm. The core goal of the algorithm is to learn to make decisions during the interaction between intelligent agents and the environment. Deep Reinforcement Learning is an algorithm that combines the perception ability of deep learning and the powerful decision-making ability of reinforcement learning. It can directly understand the network and task from experience, thus improving the accuracy and reducing repeated modeling, and is widely used to solve complex problems. A Network Service in NFV can be represented as a Service Function Chain (SFC), which consists of a series of Virtual network functions (VNF) in a given order. In order to provide efficient services, SFCS are deployed in a number of different servers and direct traffic through a set of VNFS in a specific order. This paper proposes SFC placement in NFV based on deep reinforcement learning model. Finally, it is found that the deep learning model can effectively improve the efficiency of SFC placement. © 2024 IEEE.

关键词： Network function virtualization

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Hyperspectral Image Classification Method Based on Spiking Neural Network

arXiv

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arXiv 2024年

作者： Liu, Yang Li, Yahui Li, Rui Zhou, Liming Dang, Lanxue Mu, Huiyu Ge, Qiang Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475004 China

Convolutional neural network (CNN) performs well in Hyperspectral Image (HSI) classification tasks, but its high energy consumption and complex network structure make it difficult to directly apply it to edge computing devices. At present, spiking neural networks (SNN) have developed rapidly in HSI classification tasks due to their low energy consumption and event driven characteristics. However, it usually requires a longer time step to achieve optimal accuracy. In response to the above problems, this paper builds a spiking neural network (SNN-SWMR) based on the leaky integrate-and-fire (LIF) neuron model for HSI classification tasks. The network uses the spiking width mixed residual (SWMR) module as the basic unit to perform feature extraction operations. The spiking width mixed residual module is composed of spiking mixed convolution (SMC), which can effectively extract spatial-spectral features. Secondly, this paper designs a simple and efficient arcsine approximate derivative (AAD), which solves the non-differentiable problem of spike firing by fitting the Dirac function. Through AAD, we can directly train supervised spike neural networks. Finally, this paper conducts comparative experiments with multiple advanced HSI classification algorithms based on spiking neural networks on six public hyperspectral data sets. Experimental results show that the AAD function has strong robustness and a good fitting effect. Meanwhile, compared with other algorithms, SNN-SWMR requires a time step reduction of about 84%, training time, and testing time reduction of about 63% and 70% at the same accuracy. This study solves the key problem of SNN based HSI classification algorithms, which has important practical significance for promoting the practical application of HSI classification algorithms in edge devices such as spaceborne and airborne devices. © 2024, CC BY.

关键词： Convolution

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Research on resource scheduling method based on Kubernetes

Research on resource scheduling method based on Kubernetes

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Haoyang Song Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling.

关键词： Cloud computing Schedules Clustering algorithms Containers Fish Load management Scheduling Hardware Fish schools Genetic algorithms

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Blockchain Security Research Progress and Hotspots

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第2期49卷 433页

作者： Yuan, Ke Yan, Yingjie Shen, Lin Tang, Qian Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China College of Geography and Environmental Science Henan University Kaifeng475004 China College of Cybersecurity Nankai University Tianjin300350 China

Blockchain is poised to advance the cause of product innovation and technology evolution as a novel decentralized infrastructure and distributed computing model. However, there are serious security risks in the blockchain itself, which will restrict the development of blockchain and related applications. To better grasp the current status of blockchain security research and explore the research hotspots and frontier, this study used Citespace as a bibliometric analysis tool to collect 3993 research papers between 2014 and 2020 from the WoS Core Collection as a data sample. We conduct a comprehensive overview of blockchain security research in terms of authors, research institutions, publications, countries, citations and keywords in a visual graph. This paper shows that the literature record continues to be refreshed, and it peaked in 2019. China and the USA are the main regions for collaborative research. They have established partnerships with many countries whose research institutions and authors play an important role in the collaborative network. Blockchain security research hotspots are mainly related to "cryptocurrency", "supply chain", "smart contract", "healthcare", "consensus mechanism", "fog computing", "cyber attack" and "Internet of Thing (IoT)". The lineage of blockchain development has evolved from an early focus on cryptocurrency and blockchain security risk analysis to securing other areas, such as financial systems, the IoT and healthcare through blockchain. In the future, blockchain security research will be combined with much newer technologies to form diverse developments © 2022. IAENG International Journal of Computer *** Rights Reserved.

关键词： Blockchain

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Estimation and spatiotemporal variations of black carbon aerosol over the North China Plain in 2018

Estimation and spatiotemporal variations of black carbon aer...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Cai, Kun Qi, Yongkai Li, Shenshen Zhang, Shuo College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668898

With the rapid economic development in the North China Plain, the significant emissions of black carbon (BC) aerosol have exerted notable impacts on climate, environment, and health, making it a hot topic in environmental studies in recent years. To compensate for the insufficient ground monitoring data, the PM2.5 satellite remote sensing inversion data and chemical model simulation data were utilized, applying the proportionality factor method to estimate the satellite concentration of BC. The estimation results were validated using BC monitoring data from seven stations in 2018, and based on this, the spatiotemporal variations of BC satellite concentration was obtained. The results showed that the estimated BC concentration using the proportionality factor method had good accuracy, with a correlation coefficient (R2) of 0.72. The spatial distribution of BC over the North China Plain in 2018 exhibited a decreasing trend from the inner region to the outer region, with high concentration areas mainly located in provinces with high straw burning and steel production. The seasonal variation of BC in the North China Plain in 2018 was pronounced, showing higher concentrations in autumn and winter and lower concentrations in spring and summer. © 2023 SPIE.

关键词： Satellites

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ER-MRL: Emotion Recognition based on Multimodal Representation Learning 12

ER-MRL: Emotion Recognition based on Multimodal Representati...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Guo, Xiaoding Wang, Yadi Miao, Zhijun Yang, Xiaojin Guo, Jinkai Hou, Xianhong Zao, Feifei School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

In recent years, emotion recognition technology has been widely used in emotion change perception and mental illness diagnosis. Previous methods are mainly based on single-task learning strategies, which are unable to fuse multimodal features and remove redundant information. This paper proposes an emotion recognition model ER-MRL, which is based on multimodal representation learning. ER-MRL vectorizes the multimodal emotion data through encoders based on neural networks. The gate mechanism is used for multimodal feature selection. On this basis, ER-MRL calculates the modality specific and modality invariant representation for each emotion category. The Transformer model and multihead self-attention layer are applied to multimodal feature fusion. ER-MRL figures out the prediction result through the tower layer based on fully connected neural networks. Experimental results on the CMU-MOSI dataset show that ER-MRL has better performance on emotion recognition than previous methods. © 2022 IEEE.

关键词： Emotion Recognition

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Mining Bytecode Features of Smart Contracts to Detect PonziScheme on Blockchain

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 1069-1085页

作者： Xiajiong Shen Shuaimin Jiang Lei Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China

The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in untrustworthy ***,these features of this technology are also easily exploited by unscrupulous individuals,a typical example of which is the Ponzi scheme in *** negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been *** solve this problem,we propose a detection model for detecting Ponzi schemes in smart contracts using *** this model,our innovation is shown in two aspects:We first propose to use two bytes as one characteristic,which can quickly transform the bytecode into a high-dimensional matrix,and this matrix contains all the implied characteristics in the ***,We innovatively transformed the Ponzi schemes detection into an anomaly detection ***,an anomaly detection algorithm is used to identify Ponzi schemes in smart *** results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme ***,the F1-score of this model can reach 0.88,which is far better than those of other traditional detection models.

关键词： Ponzi scheme blockchain security smart contracts anomaly detection

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