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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic Engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index 12

Multi-label Feature Selection Based on Fuzzy Neighborhood Mu...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Wang, Chenxi Chen, E. Ren, Mengli Yu, Xiehua Lin, Yaojin Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Minnan Science and Technology University School of Computer Science Lab of Data Science and Intelligence Application Quanzhou China Xiamen University School of Computer Science Lab of Data Science and Intelligence Application Xiamen China

ISBN: (纸本)9798350310153

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance. © 2022 IEEE.

关键词： Feature Selection

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Hierarchical online streaming feature selection based on adaptive ReliefF 12

Hierarchical online streaming feature selection based on ada...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Wang, Chenxi Ren, Mengli Chen, E. Yu, Xiehua Lin, Yaojin Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Minnan Science and Technology University School of Computer Science Lab of Data Science and Intelligence Application Quanzhou China Xiamen University School of Computer Science Lab of Data Science and Intelligence Application Xiamen China

ISBN: (纸本)9798350310153

In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm. © 2022 IEEE.

关键词： Feature Selection

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label Distribution Learning Based on Two-stage Model Error Repair Method 13

Label Distribution Learning Based on Two-stage Model Error R...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Mao, Yu Cai, Zhiyi Li, Yulin Shi, Chunyu School of Computer Science Minnan Normal University Zhangzhou China Minnan Normal University Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350319156

label distribution learning (LDL) is a new learning paradigm proposed in the field of machine learning to solve the problem of label ambiguity. Many existing label distribution learning algorithms only consider the simple relationship between labels and do not mine out the complex relationship between labels. To address this problem, this paper proposes a label distribution learning based on a two-stage model error repair method(LDL-TERM). First, the initial model is used to predict the training set, and its prediction results are used as additional Features are integrated into the LDL-TER model;secondly, norm regularization is used to control the relative importance between the additional feature set and the original feature set. Experiments on 14 related data sets prove that the algorithm has better performance. © 2023 IEEE.

关键词： Learning algorithms

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Multi-label Feature Selection Based on Improved Fisher Score with label Correlation 13

Multi-Label Feature Selection Based on Improved Fisher Score...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Mao, Yu Cheng, Yuxuan Shi, Chunyu Lin, Yaojin School of Computer Science Minnan Normal University Zhangzhou China Minnan Normal University Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350319156

Feature selection for multi-label classification has received extensive attention in the fields of machine learning and data mining. However, some feature selection methods fail to incorporate label correlations, resulting in the degradation of learning performance. In this paper, we dedicate to fuse the Fisher score model and neighborhood mutual information model for multi-label learning. The neighborhood mutual information is initially constructed to identify the coefficients between labels, by measuring the granularity of each instance. After that, an improved Fisher score model based on label coefficients is formed to select features with multiple crucial labels. Extensive experiments demonstrate the effectiveness and stability of the proposed method. © 2023 IEEE.

关键词： Feature Selection

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Joint Optimization of Computation Offloading, Dynamic Pricing and Admission Control in Mobile Edge Computing-Enable Blockchain 24

Joint Optimization of Computation Offloading, Dynamic Pricin...

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Yi Zhang, Wenjie Zheng, Yifeng Chen, Liming Key Lab of Data Science and Intelligence Application School of Computer Sciences Minnan Normal University Fujian Zhangzhou China Zhangzhou Affiliated Hospital of Fujian Medical University Fujian Zhangzhou China

ISBN: (纸本)9798400710834

In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing (MEC) has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types of miners and a single Edge Service Provider (ESP). Different types of miners have different resource requirements and corresponding prices. Each miner makes a computation offloading decision based on the resource price set by ESP. Different miners possess varying resource requirements and correspond to distinct prices. Miners formulate task offloading decisions based on the prices set by ESP for different resource types. The ESP dynamically adjusts resource price based on miners’ offloading requests and makes admission control according to resource usage in each time slot. The problem is a joint optimization problem of computation offloading, price strategy, and admission control, we model the problem as a sequential decision-making problem and use a dynamic programming algorithm to obtain the optimal solution. Through simulation experiments, our results demonstrate that the proposed algorithm can effectively improve the overall social utility. © 2024 Copyright held by the owner/author(s).

关键词： Computation offloading

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Resource Reservation Contract Design for Mobile Edge Computing with Energy Harvesting 4

Resource Reservation Contract Design for Mobile Edge Computi...

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4th International Conference on Artificial intelligence and Computer Engineering, ICAICE 2023

作者： Jiang, Deyue Chen, Liming Lin, Ziqiong Zhang, Wenjie Zheng, Yifeng Key Lab of Data Science and Intelligence Application School of Computer Sciences Minnan Normal University Zhangzhou China Zhangzhou Affiliated Hospital of Fujian Medical University Zhangzhou China

ISBN: (纸本)9798400708831

To address the bottleneck of energy performance, this paper considers an energy harvesting (EH)-enabled Mobile Edge Computing (MEC) system with contract users (CUs) and random users (RUs). The resource demands of CUs are known in advance, while RUs are mobile and randomly arriving. We propose a resource reservation framework based on contract theory with the goal of maximizing the utility of agent node (AN) under information asymmetry and demand uncertainty. First, for symmetric information scenario, the relationship between the optimal resource reservation and the demands of CUs is obtained. Then, for asymmetric information scenario, a contract-based resource reservation scheme is proposed to motivate wireless access point (WAP) to share the demand information of CUs and take part of over-reservation risk cost. Based on this, the theoretical expression for the optimal reserved resource is obtained by exploring the features of the feasible contracts under individual rationality and incentive compatibility constraints. The effectiveness of proposed contract-based resource reservation scheme is validated through simulation results. © 2023 ACM.

关键词： Energy harvesting

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Iterative Active-Inactive Obstacle Classification for Time-Optimal Collision Avoidance

arXiv

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arXiv 2024年

作者： Kaymaz, Mehmetcan Ure, Nazim Kemal The Data & Decision Lab. ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Turkey

Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a moving vehicle to reach its goal while navigating around obstacles within its environment. This problem becomes increasingly challenging as the number of obstacles in the environment rises. We propose an iterative active-inactive obstacle approach, which involves identifying a subset of the obstacles as"active", that considers solely the effect of the"active" obstacles on the path of the moving vehicle. The remaining obstacles are considered"inactive" and are not considered in the path planning process. The obstacles are classified as’active’ on the basis of previous findings derived from prior iterations. This approach allows for a more efficient calculation of the optimal path by reducing the number of obstacles that need to be considered. The effectiveness of the proposed method is demonstrated with two different dynamic models using the various number of obstacles. The results show that the proposed method is able to find the optimal path in a timely manner, while also being able to handle a large number of obstacles in the environment and the constraints on the motion of the object. © 2024, CC BY.

关键词： Motion planning

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Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer 23

Tracing Knowledge Instead of Patterns: Stable Knowledge Trac...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Yin, Yu Dai, Le Huang, Zhenya Shen, Shuanghong Wang, Fei Liu, Qi Chen, Enhong Li, Xin Anhui Key Lab. of Big Data Analysis and Application University of Science and Technology of China State Key Lab. of Cognitive Intelligence Anhui Hefei China University of Science and Technology of China IFLYTEK Co. Ltd China

ISBN: (纸本)9781450394161

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner's learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner's knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://***/yxonic/DTransformer. © 2023 ACM.

关键词： Deep neural networks

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Network configuration approach based on the reliability service 12

Network configuration approach based on the reliability serv...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Fengfei, Kuang Yaojm, Lin Xiehua, Yu Meilong, Chen University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Fujian Quanzhou362332 China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Wireless communication network plays an important role in various industries. One of the key concern in wireless communication is network security. This paper proposes an optimal transmission lines assignment with maximal reliabilities in the wireless communication. Firstly, a reliability evaluation metric with a transmission line assignment is proposed for calculating the reliability service under some constraints. Secondly, based on the reliability, non-dominated sorting Genetic algorithm is used for working out the optimal solution via considering the network security. Some simulated experiments are conducted that the proposed approach is able to obtain the reasonable solution under the time constraints. © 2022 IEEE.

关键词： Reliability

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