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quantum particle swarm optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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Computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya BAl-onazi Abdulkhaleq Q.A.Hassan Mohamed K.Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Ishfaq Yaseen Gouse Pasha Mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the *** SA technique is specifically applied to the data collected from social media *** research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction *** this background,the current study introduces a novel method called quantum particle swarm optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in ***,the data pre-processing is performed to convert the raw tweets into a useful ***,the word2vec model is applied to generate the feature *** Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the ***,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU *** proposed QPSODL-SAAT model was experimentally validated using the standard *** extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding

quantum particle swarm optimization for Batch Scheduling Problem of a Single Processor in Chemical Industries 25th

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Quantum Particle Swarm Optimization for Batch Scheduling Pro...

Chaos quantum particle swarm optimization for reactive power optimization considering voltage stability

第25届中国控制与决策会议

作者： Qi Tang Peng Liu Xiaoye Zhou Chuanbo Li The Department of Logistics Management Shenyang University of Technology

ISBN: (纸本)9781467355322

In this paper we consider batch scheduling problem of a single processor in chemical industries with precedence constraints to minimize the total weighted tardiness of jobs. This problem is known to be strongly NP-hard. Solution methodologies based on model and quantum particle swarm optimization （QPSO） are developed to solve it. In this approach, some strategies are designed such as Schedule Generation Scheme （SGS）. Computations show that model can obtain 100% optical solutions. QPSO is effective and better than standard PSO and quantum evolution algorithm.

关键词： quantum particle swarm optimization batch scheduling chemical industries

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Journal of Harbin Institute of Technology(New Series) 2010年第3期17卷 351-356页

作者：瞿苏寒马平蔡兴国 School of Electrical Engineering and Automation Harbin Institute of Technology School of Automation Engineering Qingdao University

The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.

关键词： reactive power optimization voltage stability margin quantum particle swarm optimization chaos optimization

An Improved quantum particle swarm optimization and Its Application in System Identification

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An Improved Quantum Particle Swarm Optimization and Its Appl...

Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

第26届中国控制与决策会议

作者： Huang yu Xiao tiantian Han pu Department of Automation North China Electric Power University

ISBN: (纸本)9781479937066

In order to improve convergence speed and precision of optimization in quantum particle swarm optimization（QPSO）, an improved quantum particle swarm optimization（IQPSO） algorithm was presented. Chaotic sequences were used to initialize the origin angle position of particle, mutation operation algorithm was used to increase diversity of population and avoid premature convergence. The proposed algorithm was applied to identify the classic adaptive infinite impulse response（IIR） model, the results show the validity of IQPSO.

关键词： quantum particle swarm optimization System identification Adaptive IIR filter

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Chinese Physics B 2008年第4期17卷 1196-1201页

作者：高飞李卓球童恒庆 Department of Science School of ScienceWuhan University of Technology Department of Engineering and Mechanics School of ScienceWuhan University of Technology

This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.

关键词： parameter estimation online chaos system quantum particle swarm optimization

Data and Model Driven Task Offloading Strategy in the Dynamic Mobile Edge Computing System

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Journal of Systems Science & Complexity 2024年第1期37卷 351-368页

作者： DONG Hairong WU Wei SONG Haifeng LIU Zhen ZHANG Zixuan School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering BeiHang UniversityBeijing 100191China CRSC Research&Design Institute Group Co. Ltd.Beijing 100070China

Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive ***,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational *** limitation becomes notably significant in complex industrial networks of high-speed *** by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge *** link switching mechanism is executed by the train according to the attributes of the completed *** task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional ***,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time *** simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

关键词： Data driven model informer mobile edge computing quantum particle swarm optimization task offloading.

Optimal operation of Internet Data Center with PV and energy storage type of UPS clusters

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Global Energy Interconnection 2024年第1期7卷 61-70页

作者： Man Chen Yuxin Zhao Yuxuan Li Peng Peng Xisheng Tang CSG PGC Energy Storage Research Institute Guangzhou 510000P.R.China Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China

With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization *** revitalization of these idle UPS resources is an urgent problem that must be *** on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this ***,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control ***,the classified EUPS aggregation unit was used to determine the optimal operation of the *** IDC cost minimization optimization model was established,and the quantum particle swarm optimization(QPSO)algorithm was ***,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

关键词： Three-tier optimization framework Energy storage type of the UPS EUPS cluster classification method quantum particle swarm optimization

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

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Frontiers of Structural and Civil Engineering 2023年第1期17卷 25-36页

作者： Xinyu WANG Jian WU Xin YIN Quansheng LIU Xing HUANG Yucong PAN Jihua YANG Lei HUANG Shuangping MIAO Yellow River Engineering Consulting Co. Ltd.Zhengzhou 450003China School of Civil Engineering Wuhan UniversityWuhan 430072China Power China Huadong Engineering Co. Ltd.Hangzhou 311122China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词： tunnel boring machine control parameter optimization quantum particle swarm optimization artificial neural network tunneling energy efficiency

Utilizing the Improved QPSO Algorithm to Build a WSN Monitoring System

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Computers, Materials & Continua 2022年第2期70卷 3529-3548页

作者： Wen-Tsai Sung Sung-Jung Hsiao Department of Electrical Engineering National Chin-Yi University of TechnologyTaichung411030Taiwan Department of Information Technology Takming University of Science and TechnologyTaipei City11451Taiwan

This research uses the improved quantum particle swarm optimization(QPSO)algorithm to build an Internet of Things(IoT)life comfort monitoring system based on wireless sensing *** purpose is to improve the quality of intelligent *** functions of the system include automatic basketball court lighting system,monitoring of infants’sleeping posture and accidental falls of the elderly,human thermal comfort measurement and other related life comfort services,*** the hardware system of the IoT,this research is based on the latest version of ZigBee 3.0,which uses optical sensors,3-axis accelerometers,and temperature/humidity sensors in the IoT perception *** the network transmission layer,the central network architecture is used for *** the application layer,we have designed a graphical interface for real-time values and information that can be read at any time and place using mobile *** this study,authors use the improved QPSO algorithm in the calculation part,so that the target can be effectively positioned outside the numerous surveillance *** study uses various sensor data fusion technologies to make the IoT system becomes able to provide more extensive and even better services than ever *** short,this research work has proven to be an effective way to reduce power consumption,improve medical quality and provide higher comfort for intelligent lift level.

关键词： quantum particle swarm optimization IoT wireless sensor network ZigBee