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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Multi-CNN Model for Multi-Classification of Cultural Heritage Monuments 6

Multi-CNN Model for Multi-Classification of Cultural Heritag...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Djelliout, Toufik Aliane, Hassina University of Bejaia Faculty of Exact Sciences Department of Computer Science Algeria Research Center For Scientific And Technical Information Algiers Algeria

ISBN: (纸本)9798350350265

The use of convolutional neural networks (CNN) in the preservation of cultural heritage monuments, especially in conflict-affected regions such as Gaza, Ukraine, Iraq and others, represents a significant advancement in heritage conservation efforts. This paper presents an approach that uses a Multi-CNN model to classify images of cultural heritage monuments into various categories, encompassing period, monument type and location. By leveraging the capabilities of CNNs, this model demonstrates a high level of accuracy in categorizing heritage monuments based on multiple attributes. The study highlights the superior performance of the Multi-CNN model compared to other popular models such as DenseNet169, GoogleNet and MnasNet, highlighting its effectiveness in accurately classifying images of cultural heritage monuments in various dimensions. According to the evaluation results, the top-performing multi-CNN model achieves a classification accuracy of 94.52%, outperforming the single CNN models. The DenseNet196 model achieves 93.70% accuracy, the MnasNet model achieves 92.80% accuracy, and the GoogleNet model achieves 88.18% accuracy. © 2024 IEEE.

关键词： Neural network models

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EEVEE: An Easy Annotation Tool for Natural Language Processing 18

EEVEE: An Easy Annotation Tool for Natural Language Processi...

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18th Linguistic Annotation Workshop, LAW 2024

作者： Sorensen, Axel Peng, Siyao Plank, Barbara van der Goot, Rob Department of Computer Science IT University of Copenhagen Denmark Munich Germany MaiNLP Center for Information and Language Processing LMU Munich Germany

ISBN: (纸本)9798891760738

Annotation tools are the starting point for creating Natural Language Processing (NLP) datasets. There is a wide variety of tools available;setting up these tools is however a hindrance. We propose EEVEE, an annotation tool focused on simplicity, efficiency, and ease of use. It can run directly in the browser (no setup required) and uses tab-separated files (as opposed to character offsets or task-specific formats) for annotation. It allows for annotation of multiple tasks on a single dataset and supports four task-types: sequence labeling, span labeling, text classification and seq2seq. © 2024 Association for Computational Linguistics.

关键词： Classification (of information)

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Urdnet:A Cryo-EM Particle Automatic Picking Method

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computers, Materials & Continua 2022年第7期72卷 1593-1610页

作者： Jianquan Ouyang Yue Zhang Kun Fang Tianming Liu Xiangyu Pan School of Computer Science&School of Cyberspace Science Xiangtan UniversityXiangtan411105China Hunan Meteorological Information Center Hunan Meteorological BureauChangshaHunan410118China Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

Cryo-Electron Microscopy(Cryo-EM)images are characterized by the low signal-to-noise ratio,low contrast,serious background noise,more impurities,less data,difficult data labeling,simpler image semantics,and relatively fixed structure,while U-Net obtains low resolution when downsampling rate information to complete object category recognition,obtains highresolution information during upsampling to complete precise segmentation and positioning,fills in the underlying information through skip connection to improve the accuracy of image segmentation,and has advantages in biological image processing like Cryo-EM *** article proposes A U-Net based residual intensive neural network(Urdnet),which combines point-level and pixel-level tags,used to accurately and automatically locate particles from cryo-electron microscopy images,and solve the bottleneck that cryo-EM Single-particle biologicalmacromolecule reconstruction requires tens of thousands of automatically picked *** 80S ribosome,HCN1 channel and TcdA1 toxin subunits,and other public protein datasets have been trained and tested on *** experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION,DeepPicker,and acquire the 3Dstructure of picked particleswith higher resolution.

关键词： Deep learning convolutional neural network particle picking cryo-electron microscopy single-particle reconstruction

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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The coordination and leadership in first aid ad-hoc immediate responder groups: a work-in-progress 21

The coordination and leadership in first aid ad-hoc immediat...

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21st International ISCRAM Conference, ISCRAM 2024

作者： Brodin, Wilhelm Jonson, Carl-Oscar Prytz, Erik Department of Computer and Information Science Linköping University Sweden Center for Disaster Medicine and Traumatology Linköping University Sweden

When an accident has occurred, first aid before a professional response arrives can improve the outcome for the victims. However, little research has been done on the effect of coordination and leadership on first aid responses. This work-in-progress study reports an experiment using a simulated scenario, i.e. traffic accident, to understand the effect of coordination and leadership on first aid performance in ad-hoc groups of immediate responders. The results showed that coordination and leadership are significantly related to increased self-evaluated team performance while expertise and authority differentiation is not. These results provide a first insight to the coordination and leadership in ad-hoc groups of immediate responders, but more research is needed to validate these results. Structured performance evaluation with subject matter experts is planned as an extension to the present work-in-progress. © 2024 information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： information use

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Improvement of Spiking Neural Network with Bit Plane Coding 16

Improvement of Spiking Neural Network with Bit Plane Coding

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Luu, Nhan T. Luu, Duong T. Nam, Pham Ngoc Thang, Truong Cong The University of Aizu Department of Computer Science and Engineering Aizuwakamatsu Japan Can Tho University Information and Network Management Center Can Tho Viet Nam College of Engineering and Computer Science VinUniversity Hanoi Viet Nam

ISBN: (纸本)9798331505264

Spiking neural networks (SNNs) have gained sig-nificant attention as a promising framework in computational neuroscience and artificial intelligence, offering benefits such as low energy consumption and minimal memory requirements. Despite these advantages, their practical deployment is hindered by several challenges, particularly in performance optimization. This paper introduces a novel methodology to enhance the performance of SNNs in image processing by leveraging a bit-plane-based coding scheme. The proposed approach is specifically designed to improve the accuracy of SNNs without increasing their model size. Comprehensive experimental evaluations demonstrate the efficacy of this coding strategy across multiple datasets. To the best of our knowledge, this work is the first to explore the application of bit-plane representation in the context of SNNs. © 2024 IEEE.

关键词： Network coding

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Belief Combination of Classifiers for Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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