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Synergistic Swarm Optimization Algorithm

computer Modeling in engineering & sciences 2024年第6期139卷 2557-2604页

作者： Sharaf Alzoubi Laith Abualigah Mohamed Sharaf Mohammad Sh.Daoud Nima Khodadadi Heming Jia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Al Al-Bayt UniversityMafraq25113Jordan Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia College of Engineering Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Civil and Architectural Engineering University of MiamiCoral Gables1251USA School of Information Engineering Sanming UniversitySanming365004China

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search *** cooperation enhances the exploitation of promising regions in the search space while maintaining exploration ***,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and *** leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization *** effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design *** experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and *** codes of SSOA are available at:https://***/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

关键词： Synergistic swarm optimization algorithm optimization algorithm metaheuristic engineering problems benchmark functions

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Future Communication Technology Using Huge Millimeter Waves - An Overview 1

Future Communication Technology Using Huge Millimeter Waves ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Banda, Latha Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798350338027

Frequency selective surface (FSS) was first created by G. Marconi and C. S. Franklin in 1919 and is known as the "Reflector for Wireless Telegraph and Telephone,"making a significant contribution to our way of life. In terms of the 1960s, it is impossible to overlook the success attained with the employment of regularly selective surfaces in military intelligence units. These structures, which were occasionally applied to land, air, and sea platforms, displayed low pass, high pass, band pass, or band stop characteristics for particular frequencies and served as electromagnetic filters. Many accomplishments have been made, particularly with platforms that are invisible to target detection systems. The geometry and arrangement of the unit cell in these structures, which are successfully used in many fields such as wireless communication systems, radars, and microwaves, allows for the reflection or transmission of the incoming electromagnetic wave. This is made possible by the advancement of technology. High-speed internet connectivity is possible using millimeter wave, a frequency spectrum between 30 GHz and 300 GHz. Its significance has grown in the fields of radio astronomy, radar, remote sensing, and wireless communications. © 2023 IEEE.

关键词： Millimeter waves

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Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

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Digital Communications and Networks 2023年第2期9卷 296-305页

作者： Jing Zhang Qichen Yan Xiaogang Zhu Keping Yu Department of Computer Science and Technology Xi'an University of Science and TechnologyXi'an710054China Department of Information Science and Technology Northwest UniversityXi’an 710127China School of Public Policy and Administration Nanchang UniversityNanchangJiangxi330031China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence ***,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not *** solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal ***,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position ***,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground *** them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,***,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

关键词： Crowd sensing Industrial Internet of things Safety monitoring Coal mine

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Active Learning for High-Quality Sawtooth Wave Dataset Generation in Sleep Analysis 6

Active Learning for High-Quality Sawtooth Wave Dataset Gener...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Chen, Jinghuang Bao, Xiaoyu Ye, Yuntao Huang, Qiyun School of Automation Science and Engineering South China University of Technology Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China

ISBN: (纸本)9798331541750

Sawtooth Waves (STW) that occur during Rapid Eye Movement (REM) sleep are closely associated with memory consolidation and the regulation of brain functions. However, due to challenges in data acquisition and the high cost of manual annotation, there is currently an absence of a publicly massive STW annotated dataset. This study focuses on constructing a high-quality STW dataset, comprising 4912 samples from 200 participants, and developing classification algorithms for STW detection. We utilized three prevalent algorithms-Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-to assess the dataset's quality. The dataset achieved a maximum classification accuracy of 95.62% with the CNN, confirming the gold standard of STW dataset. Finally, we studied age and gender difference in physiological characteristics of STW, including average frequency of STW, peak-to-peak amplitude(PTP), root mean square(RMS) and spectrum power. This study created a public STW dataset and a STW detection model, laying the groundwork for analyzing the mechanisms of STW events. © 2024 IEEE.

关键词： Convolutional neural networks

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Terahertz deep learning fusion computed tomography

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Optics Express 2024年第10期32卷 17763-17774页

作者： Hung, Yi-Chun Su, Weng-Tai Chao, Ta-Hsuan Lin, Chia-Wen Yang, Shang-Hua Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Department of Computer Science McCormick School of Engineering and Applied Science Northwestern University EvanstonIL60208 United States Institute of Electronics Engineering National Tsing Hua University Hsinchu30013 Taiwan Terahertz Optics and Photonics Center National Tsing Hua University Hsinchu30013 Taiwan

Terahertz (THz) tomographic imaging based on time-resolved THz signals has raised significant attention due to its non-invasive, non-destructive, non-ionizing, material-classification, and ultrafast-frame-rate nature for object exploration and inspection. However, the material and geometric information of the tested objects is inherently embedded in the highly distorted THz time-domain signals, leading to substantial computational complexity and the necessity for intricate multi-physics models to extract the desired information. To address this challenge, we present a THz multi-dimensional tomographic framework and multi-scale spatio-spectral fusion Unet (MS3-Unet), capable of fusing and collaborating the THz signals across diverse signal domains. MS3-Unet employs multi-scale branches to extract spatio-spectral features, which are subsequently processed through element-wise adaptive filters and fused to achieve high-quality THz image restoration. Evaluated by geometry-variant objects, MS3-Unet outperforms other peer methods in PSNR and SSIM. In addition to the superior performance, the proposed framework additionally provides high scalable, adjustable, and accessible interface to collaborate with different user-defined models or methods. © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Adaptive filters

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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Deciphering urban traffic impacts on air quality by deep learning and emission inventory

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Journal of Environmental sciences 2023年第2期124卷 745-757页

作者： Wenjie Du Lianliang Chen Haoran Wang Ziyang Shan Zhengyang Zhou Wenwei Li Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Alibaba Inc. Hangzhou 310052China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China CAS Key Laboratory of Urban Pollutant Conversion Department of environmental science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou 215123China

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future ***,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air *** model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other *** identifies the ranking of major factors,local meteorological conditions have become a nonnegligible ***-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of ***,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our *** also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 *** insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.

关键词： PM_(2.5)concentration forecast Traffic emissions Deep learning Attention mechanism New energy vehicles

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Leveraging ChatGPT for enhanced stock selection and portfolio optimization

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Neural Computing and Applications 2025年第8期37卷 6163-6179页

作者： Huang, Zhendai Liao, Bolin Hua, Cheng Cao, Xinwei Li, Shuai College of Computer Science and Engineering Jishou University Jishou416000 China School of Business Jiangnan University Wuxi214122 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland VTT-Technology Research Center of Finland Kaitovayla 1 Oulu90570 Finland

The public release of ChatGPT represents a significant milestone in generative AI technology, enabling the autonomous generation of content based on pre-training. This breakthrough presents new opportunities for advancements in the field of portfolio selection. This paper aims to introduce a comprehensive portfolio selection method by applying ChatGPT for stock selection and combining it with optimization algorithms to jointly optimize portfolio selection. Compared to randomly selected stocks, the portfolios optimized using ChatGPT-selected stocks and solved with the egret swarm optimization algorithm (ESOA) demonstrate higher diversification and lower volatility, leading to superior portfolio optimization results. Additionally, to validate ESOA’s superiority, its performance is compared against genetic algorithm (GA) and particle swarm optimization (PSO) on five metrics: risk, expected return, Sharpe ratio, objective value, and penalty term. Under equivalent experimental setting, ESOA exhibits a better ability to balance the relationship between risk and return. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Optimization algorithms

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Boosting efficiency and stability of 2D alternating cation perovskite solar cells via rational surface-modification: Marked passivation efficacy of anion

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Journal of Energy Chemistry 2023年第9期84卷 354-362页

作者： Hualin Zheng Xuefeng Peng Tingxi Chen Ting Zhang Shihao Yuan Lei Wang Feng Qian Jiang Huang Xiaodong Liu Zhi David Chen Yanning Zhang Shibin Li State Key Laboratory of Electronic Thin Films and Integrated Devices and School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Department of Electrical&Computer Engineering and Center for Nanoscale Science&EngineeringUniversity of KentuckyLexingtonKY 40506USA Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808GuangdongChina

Two-dimensional(2D) alternating cation(ACI) perovskite surface defects,especially dominant iodine vacancies(V_Ⅰ) and undercoordinated Pb^(2+),limit the performance of perovskite solar cells(PVSCs).To address the issue,1-butyl-3-methylimidazolium trifluoro-methane-sulfonate(BMIMOTF) and its iodide counterpart(BMIMI) are utilized to modify the perovskite surface *** find that BMIMI can change the perovskite surface,whereas BMIMOTF shows a nondestructive and more effective defect passivation,giving significantly reduced defect density and suppressed charge-carrier nonradiative *** mainly attributes to the marked passivation efficacy of OTF-anion on V_Ⅰ and undercoordinated Pb^(2+),rather than BMIMI^(+) *** from the rational surface-modification of BMMIMOTF,the films exhibit an optimized energy level alignment,enhanced hydrophobicity and suppressed ion ***,the BMIMOTF-modified devices achieve an impressive efficiency of 21.38% with a record open-circuit voltage of 1.195 V,which is among the best efficiencies reported for 2D PVSCs,and display greatly enhanced humidity and thermal stability.

关键词： 2D ACI perovskite solar cells Charge-carrier nonradiative recombination Surface defects passivation Energy level alignment Ionic migration Stability

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A Review of Multi-Robot Collaborative Simultaneous Localization and Mapping

A Review of Multi-Robot Collaborative Simultaneous Localizat...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Zhao, Xianzhe Shao, Shiliang Wang, Ting Fang, Chuxi Zhang, Jin Zhao, Hai School of Computer Science and Engineering Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350316308

Collaborative Simultaneous Localization and Mapping (C-SLAM), commonly known as multi-robot SLAM, is a prefatory technology that allows multiple robots to efficiently traverse large and intricate areas. This is possible due to the merging of maps and the recognition of shared spaces. Despite its many benefits, multi-robot systems encounter various challenges, such as communication barriers, co-localization issues, and joint optimization problems. This paper reviews the sensors and back-end optimization methods used for SLAM. Then, it summarizes existing collaborative SLAM frameworks and some novel solutions. Finally, it discusses trends and promising research directions for collaborative SLAM. © 2023 IEEE.

关键词： Multipurpose robots

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