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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Wang, Qianhui Ye, Yulong Zhu, Qingling Liu, Songbai Lin, Qiuzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350393521

Dynamic constrained multi-objective optimization problems (DCMOPs) are characterized by time-varying objectives and constraints, requiring optimization algorithms that can rapidly track the changing Pareto-Optimal Set (POS).A new dynamic constrained multi-objective evolutionary algorithm with adaptive two-stage archiving and autoencoder prediction is proposed in this paper, called ATAP to effectively solve DCMOPs. Specifically, ATAP designs a differential denoising autoencoder (DDA) prediction strategy, which applies a denoising autoencoder to thoroughly analyze change trends of historical population and predict some initial solutions in the new environment. Subsequently, to promote greater diversity within the initial population, a differential rule is implemented, effectively addressing the potential scarcity of diversity caused by constraints. Moreover, ATAP introduces an adaptive two-stage archiving (ATA) constraint handling technique, which can dynamically adjust evolutionary stages based on the state of the population. This approach can adaptively determine preferences between objectives and constraints, achieving a better balance. In this way, ATA and DDA are well cooperated to efficiently solve DCMOPs with time-varying constraints and objectives. The experimental results demonstrate that the proposed ATAP is effective and has some advantages over three competitive algorithms when solving the CEC2023 DCMOP benchmark. © 2023 IEEE.

关键词： Forecasting

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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SupervisedImmuneNet: Training Artificial Immune Networks using a Supervised Learning Approach for Improved Multi-Class Classification 6

SupervisedImmuneNet: Training Artificial Immune Networks usi...

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6th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2023

作者： Sithungu, Siphesihle Philezwini Ehlers, Elizabeth Marie Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9798400709067

The most common application of artificial immune networks (AINs) is on unsupervised learning tasks. This is due to the fact that AINs are inspired by the adaptive immune system, which consists of a network of antibodies that self-organises to form a memory of external antigens. The self-organising nature of AINs makes them a natural approach for solving problems involving learning and adapting to patterns or structures present in a dataset to form an abstract representation. Training AINs in this fashion means that the dataset need not have class labels because the typical aim of the learning process is not to perform classification. However, there have been attempts to use AINs for classification tasks by considering the resulting clusters of antibodies as representative of the classes present in a dataset. This has also been done when applying AINs to the task of recognising handwritten characters. However, in all the approaches found in the literature, the common method was to leave the task of discovering classes to the AINs. Doing so is contrary to how other models are trained to do classification tasks where data samples are provided along with their class labels to guide the learning process. Therefore, this paper presents a novel supervised learning approach to training AINs for multi-class classification. The proposed approach was tested on the MNIST handwritten digits dataset and achieved a classification accuracy of 99.45%. © 2023 ACM.

关键词： Antibodies

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A dynamic and resource sharing virtual network mapping algorithm

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Digital Communications and Networks 2023年第5期9卷 1101-1112页

作者： Xiancui Xiao Xiangwei Zheng Ji Bian Cun Ji Xinchun Cui School of Information Science and Engineering Shandong Normal UniversityJinan250014China School of Information Engineering Shandong Management UniversityJinan250358China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014China School of Computer Science and Technology Qufu Normal UniversityRizhao276800China

Network virtualization can effectively establish dedicated virtual networks to implement various network ***,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and *** addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation *** on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology *** the node mapping process,three properties of the node are used to measure its mapping ***,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link ***,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource *** former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision *** results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenu

关键词： Network virtualization VNRs Network frameworks Dynamic resource allocation Resource sharing

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Compression Algorithm Selection for Column-Oriented Databases Based on Stacking 11

Compression Algorithm Selection for Column-Oriented Database...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Tian, Fuwen Xu, Meng College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350333664

Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, and records within the same attribute column often exhibit high similarity, providing favorable conditions for data compression. Researchers have proposed numerous compression algorithms, but these algorithms can only achieve optimal compression results when applied to data with specific features. Therefore, it is crucial to select the compression algorithm that performs best based on the features of the data. We propose a novel method for selecting compression algorithms using Stacking, a technique that effectively enhances the prediction ability of classification models in machine learning. Our experimental results demonstrate that the stacked model achieves an accuracy of 92.9%, surpassing other models in terms of F1-Score and Kappa coefficient. Furthermore, our compression algorithm selection method outperforms other existing methods in terms of compression performance. © 2023 IEEE.

关键词： classification model column-oriented database data compression Stacking

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An interval RSP-based ensemble model for big data analysis 29

An interval RSP-based ensemble model for big data analysis

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29th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2023

作者： Cai, Wenzhu Ao, GengYuan Lin, YiGang Li, Mark Junjie College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)1891706578

Ensemble learning for big data has been successful in machine learning and has great advantages over other learning methods. The ensemble model based on Random Sample Partition (RSP) is a prominent method of it. Although the RSP data blocks have the consistent probability distribution function as the whole data, there is some uncertainty in prediction results due to the non-overlapping data between blocks. In this paper, we propose a novel interval ensemble model based on RSP named Inr-RSP, which maps prediction results to interval-valued data by interval modeling and then uses the IAA aggregation method to convert the interval-valued data into fuzzy sets to get a more accurate and stable final result. The experimental classification results from four real datasets also show that the performance of this model is better than that of the traditional RSP ensemble model. And the IAA method usage has a stronger ability to capture uncertainty in prediction than the common majority voting method. Copyright © 2023 by KSI Research Inc. and Knowledge Systems Institute, USA.

关键词： Learning systems

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Learning Contention Window Selection in Age of Information-Oriented IEEE 802.11 Networks 11

Learning Contention Window Selection in Age of Information-O...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Li, Jinhua Jian, Feng Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350314014

We study the problem of selecting the contention window (CW) for age of information (AoI)-oriented IEEE 802.11 networks using deep reinforcement learning (DRL) techniques. AoI quantifies information freshness and is defined as the time elapsed since the generation of the latest received information update. As the number of stations increases, conventional CW selection methods defined in IEEE 802.11 standards, such as binary exponential backoff (BEB), fail to achieve a low network-wide average AoI. Therefore, we leverage DRL and propose a CW control algorithm based on deep deterministic policy gradient (DDPG) that can dynamically learn appropriate CW values under different network conditions. Simulation results demonstrate that our proposed DRL-based approach reduces the average AoI of the system by about 57.6% in a congested network with 50 stations compared to the conventional BEB algorithm. Furthermore, compared to the algorithm using throughput as the DRL reward function, our AoI-reward-function approach achieves a 20.4% improvement in information freshness, indicating the key difference between AoI and throughput. © 2023 IEEE.

关键词： Reinforcement learning

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Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling 4

Deep Hierarchy Quantization Compression algorithm based on D...

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4th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Jiang, Wan Liu, Gang Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350314496

Federated learning allows multiple parties to jointly train deep learning models without the need for any participants to reveal their private data to a centralized server. However, this form of privacy-preserving collaborative learning has resulted in significant communication costs during the training period. To address this problem, we propose the Deep Hierarchical Quantization Compression (DHQC) algorithm. DHQC reduces the communication cost by using gradient sparsification and multi-bit quantization, and improves the accuracy and convergence speed of the model by using a dynamic sampling strategy. Experimental results on the CIFARIO and CIFARIOO datasets demonstrate that compared with the uncompressed baseline, DHQC can achieve a compression rate of 128x, significantly reducing the communication costs. © 2023 IEEE.

关键词： Deep learning

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Detection and Tracking of Pinus Radiata Catkins 1

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37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022

作者： Song, Eric Schofield, Sam Green, Richard Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9783031258251

ISBN: (纸本)9783031258244

Pinus Radiata trees form pollen-producing catkins that can be harvested for pharmaceutical uses. Unmanned Aerial Vehicles (UAVs) may be well suited to the task of autonomously harvesting these catkins. We propose a method to reliably detect and track P. Radiata catkins in three dimensions that can be used for real-time guidance of a UAV. The method applies the YOLOv5 deep learning algorithm to detect catkins in the X-Y plane. A novel optimisation of the MeanShift algorithm is utilised to assist existing contour detection algorithms in segmenting individual catkins in the Z plane. A Kanade-Lucas-Tomasi tracker was used with RANSAC for accurate frame-to-frame tracking. The method achieved a Mean Average Precision of 0.87 on images taken at a commercial pine pollen farm. The method detected the depth of catkins at distances of up to 1200 mm to an accuracy of 2 mm, or 8 mm for occluded catkins. Detected catkins can be reliably tracked at speeds of 1ms. An average frame rate of 22 frames per second was achieved on an Intel i5 CPU, with the Meanshift optimisation performing up to 41 times faster than existing implementations. These results indicate that the proposed method could be used to successfully assist in the automated harvesting of P. Radiata catkins. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Legal Judgment Prediction Incorporating Guiding Cases Matching 12th

Legal Judgment Prediction Incorporating Guiding Cases Matchi...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Li, Hengzhi Cai, Shubin Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031446924

Legal judgment prediction aims to predict the judgment result based on the case fact description. It is an important application of natural language processing within the legal field. To enhance the impartiality and consistency of the judiciary, the Supreme People’s Court selects representative cases and categorizes them as guiding or typical cases. However, the current research is based on the unified training of large-scale datasets, and does not distinguish the value of different existing cases. To address these issues, this paper collects relevant guiding cases and typical cases, designs a legal judgment prediction method that incorporates matching guiding cases, and proposes a legal judgment prediction model called MAGIC (MAtching GuIding Cases), based on multi-feature fusion. The goal of this research is to improve the prediction performance of legal judgments by utilizing the value of high-quality cases. The experimental results on the CAIL2018 evaluation datasets demonstrate that the proposed method significantly improves the performance of legal judgment prediction models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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