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An Improved quantum particle swarm optimization algorithm for Target Tracking Deployment in Spatial Sensor Networks

JOURNAL OF INTERNET TECHNOLOGY 2024年第5期25卷 709-721页

作者： Liu, Lisha Fan, Xincan Shenzhen Polytech Univ Dept Integrated Circuits Shenzhen Peoples R China

This paper presents a comprehensive review of the current research on spatial sensor networks and the node deployment methods employed for mobile target tracking. The study introduces particle swarm optimization (PSO) and its quantum behavior extension, detailing concepts such as the quantum state wave function and particle position representation. Subsequently, an improved quantum particle swarm optimization (QPSO) algorithm is proposed. This enhanced algorithm increases population diversity by incorporating quantum rotation gates and quantum mutation mechanisms, expands the search space through the superposition state and interference principles of quantum mechanics, and dynamically adjusts algorithm parameters to balance global exploration and local search. These modifications aim to improve both the convergence speed and accuracy of the algorithm. Simulation results demonstrate that the improved QPSO algorithm surpasses traditional mobile tracking deployment algorithms and the standard quantum behavior particle swarm optimization algorithm in terms of target tracking deployment within spatial sensor networks. Notably, it significantly enhances the tracking success rate and reduces tracking errors.

关键词： Spatial sensor network Target tracking Node deployment quantum particle swarm optimization algorithm

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A quantum particle swarm optimization algorithm with Available Transfer Capability 19

A Quantum Particle Swarm Optimization Algorithm with Availab...

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19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Qu Liping Meng Yan Li Dongheng Xue Hai-bo Beihua Univ Engn Training Ctr Jilin 132011 Jilin Peoples R China Jilin Prov Ind Technician Coll Dept Elect Engn Jilin 132011 Jilin Peoples R China State Grid Jilin Elect Power Co Ltd Shulan Power Supply Co Jilin 132011 Jilin Peoples R China Jilin Municipal Govt Jilin 132011 Jilin Peoples R China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197241

quantum particle swarm algorithm integrated the quantum behavior with particle swarm optimization algorithm, is used to settle the majorization question of calculating available transmission capability. And by using the software of Matlab to IEEE-30 bus system as an example of the simulation, after comparing the simulation results with the traditional particle swarm optimization algorithm results, we dissected the optimization performance and convergence speed of the above two algorithms, and verify the effectiveness of quantum particle swarm algorithm to settle the majorization question of the available transmission capability.

关键词： available transmission capacity particle swarm optimization algorithm quantum particle swarm optimization algorithm

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Combined quantum particle swarm optimization algorithm for Multi-objective Nutritional Diet Decision Making

Combined Quantum Particle Swarm Optimization Algorithm for M...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Lv, Youbo Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China

ISBN: (纸本)9781424445189

A assembled method based on quantum particle swarm optimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multi-objective nutritional diet decision making problem. To realize nutritional diet decision optimization for patients, BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database. QPSO algorithm is selected as the core optimization algorithm to avoid being trapped in a local optimum. Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.

关键词： Nutritional Diet Decision Making quantum particle swarm optimization algorithm Multi-Objective optimization

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Optimized Dispatch of Micro Energy Grid Based on Improved quantum particle swarm optimization algorithm

Optimized Dispatch of Micro Energy Grid Based on Improved Qu...

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Chinese Automation Congress (CAC)

作者： Jia, Rong Hou, Xuqian Zhang, Huizhi Dong, Kaisong Wang, Kaiyan Xian Univ Technol Elect Power Res Inst Xian Shaanxi Peoples R China Xian Univ Technol Xian Key Lab Intelligent Energy Xian Shaanxi Peoples R China Gansu Prov Elect Power Res Inst Lanzhou Gansu Peoples R China

ISBN: (纸本)9781728113128

Through the analysis of the typical CCHP structure and the consideration of the daily operation cost and environmental cost of the micro energy network, the wind-natural-storage combined synchronous generation model including electric, flue gas, air and hot water as the basic busbar is established. The optimization results of cold, heat and electric load scheduling of micro energy network in different scenarios are analyzed. By using the quantum particle swarm optimization algorithm (QPSO) to chaotic search, neighborhood mutation and weighted updating of the optimal position center of the population, the particles are effectively prevented from falling into the local optimal solution too early when searching, thus improving the population quality and convergence speed. The analysis results show that the proposed algorithm can effectively solve the CCHP model, reduce the operating cost and environmental cost under the reliable operation of the load demand and micro-energy network, improve the utilization rate of renew able energy and realize the economic dispatch of the micro energy network.

关键词： micro energy grid CCHP optimized dispatch quantum particle swarm optimization algorithm

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A Novel Non-intrusive Load Monitoring Method Based on quantum particle swarm optimization algorithm 11

A Novel Non-intrusive Load Monitoring Method Based on Quantu...

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11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Ding, Guofeng Wu, Chenghuan Wang, Yang Liang, Yujie Jiang, Xuedong Li, Xiaobo State Grid Ningbo Elect Power Co Ningbo 315000 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728121659

Non-intrusive load monitoring (NILM) can provide rich power consumption data for home users and power supply companies, which is helpful to use the electricity in an efficient way. This paper proposes a novel NILM method based on quantum particle swarm optimization (QPSO) algorithm. First, the parameters which can represent the load characteristics of household appliances are presented. Then, the detailed NILM method based on QPSO algorithm is described. At last, experiments are carried out on a house with 8 kinds of appliances. Results show that the proposed NILM method is effective, and QPSO algorithm performs better than that of basic PSO algorithm.

关键词： Non-intrusive load monitoring quantum particle swarm optimization algorithm load identification rate

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K-means Clustering Based on Improved quantum particle swarm optimization algorithm 13

K-means Clustering Based on Improved Quantum Particle Swarm ...

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13th International Conference on Advanced Computational Intelligence (ICACI)

作者： Bai, Lili Song, Zerui Bao, Haijie Jiang, Jingqing Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Peoples R China Inner Mongolia Univ Nationalities Coll Math & Phys Tongliao Peoples R China

ISBN: (纸本)9781665412544

In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.

关键词： K-means clustering algorithm quantum particle swarm optimization algorithm QPSO-K-means algorithm Cloning Mutation

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Structural Damage Recognition Based on the Finite Element Method and quantum particle swarm optimization algorithm

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IEEE ACCESS 2020年 8卷 184785-184792页

作者： Zhang, Xin Zhang, Junjie Hu, Yutao Tang, Tao Yang, Jianhai Zhang, Yuxiang Northwest Inst Nucl Technol Xian 710024 Peoples R China Xian Res Inst Hitech Xian 710025 Peoples R China

Structural damage recognition is always the concerned focus in many fields like aerospace, petroleum and petrochemical industry, industrial production and civil life. For damage recognition in complex structure or structural interior, especially somewhere sensors can't go, minor damage is often hard identified by not only traditional nondestructive testing methods like ultrasonic testing, radiographic testing, magnetic particle testing, penetrant testing, eddy current testing, but also the current popular ultrasonic guided wave based on the piezoelectric wafer, electromagnetic acoustic transducer or magnetostrictive sensor, which is mainly because the response signals are always affected by many structural features. In this article, the advanced global search algorithm, quantum particle swarm optimization algorithm is first combined with the finite element method to accurately recognize the structural damage based on the conductance-frequency spectrum resulted from electromechanical impedance method. Meanwhile, the objective function is designed to compare the difference of peak frequency variations in the experiment and finite element calculation respectively. By adopting the stiffness reduction method of the elements near the structural damage, the identification efficiency is largely improved for no need to repeatedly partition the model grid. And after multiple iteration optimization of the artificial intelligence algorithm - quantum particle swarm optimization algorithm, the identification error of damage parameters including location and degree can be reduced to below 4 percent. Therefore, the combination of finite element method and quantum particle swarm optimization algorithm is quite effective for guaranteeing high accuracy and efficiency for damage parameters' recognition in complex structures.

关键词： quantum particle swarm optimization algorithm finite element simulation electromechanical impedance structural damage identification conductance signal

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Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy

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COMPUTERS & GEOSCIENCES 2023年 174卷

作者： Jiao, Junjun Cheng, Jiulong Liu, Yuben Yang, Haiyan Tan, Dingrui Cheng, Peng Zhang, Yuqi Jiang, Chenglin Chen, Zhi China Univ Min & Technol State Key Lab Coal Resources & Safe Min Beijing 100083 Peoples R China China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Jiangsu Peoples R China Shandong Zhengyuan Geophys Informat Technol Co Ltd Jinan 250101 Shandong Peoples R China

The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particle swarm optimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algo-rithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water -accumulated goaf.

关键词： Transient electromagnetic method Goaf Elite opposition-based learning Inversion quantum particle swarm optimization algorithm

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Combined quantum particle swarm optimization algorithm for Multi-objective Nutritional Diet Decision Making

Combined Quantum Particle Swarm Optimization Algorithm for M...

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2009 2nd IEEE International Conference on Computer Science and Information Technology（ICCSIT 2009）

作者： Youbo Lv School of Computer and Information Engineering Harbin University of Commerce Harbin,China

A assembled method based on quantum particle swarm optimization(QPSO) algorithm combined with Bayesian networks(BN) is proposed to solve complex multiobjective nutritional diet decision making *** realize nutritional diet decision optimization for patients,BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database,QPSO algorithm is selected as the core optimization algorithm to avoid being trapped in a local optimum,Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.

关键词： Nutritional Diet Decision Making quantum particle swarm optimization algorithm Multi-Objective optimization

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Combined quantum particle swarm optimization algorithm for Multi-objective Nutritional Diet Decision Making

Combined Quantum Particle Swarm Optimization Algorithm for M...

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2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

作者： Youbo Lv School of Computer and Information Engineering Harbin University of Commerce Harbin China

A assembled method based on quantum particle swarm optimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multiobjective nutritional diet decision making problem. To realize nutritional diet decision optimization for patients, BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database. QPSO algorithm is selected as the core optimization algorithm to avoid being trapped in a local optimum. Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.

关键词： Nutritional Diet Decision Making quantum particle swarm optimization algorithm Multi-Objective optimization

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