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An Adaptive Fuzzy quantum Behavior particle swarm optimization algorithm for Mobile Charging Scheduling in Wireless Rechargeable Sensor Networks 6th

An Adaptive Fuzzy Quantum Behavior Particle Swarm Optimizati...

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6th Conference on Intelligent Networked Things (CINT)

作者： Liao, Boyang Jiang, Chengpeng Xiao, Wendong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Guangdong Peoples R China

ISBN: (纸本)9789819739479;9789819739486

The utilization of mobile chargers equipped with Wireless Energy Transmission (WET) devices to charge sensors in a Wireless Rechargeable Sensor Network (WRSN) has emerged as a promising solution to address the energy constraints of the network. The charging performance of a Wireless Rechargeable Sensor Network (WRSN) relies heavily on the efficiency of the mobile charging scheduling (MCS) strategy. However, designing an effective MCS strategy to achieve efficient wireless energy replenishment poses a challenge. Existing work often assumes that sensors will not be recharged once their energy is depleted, neglecting the fact that sensors can recover after being charged in practical applications. This paper addresses the mobile charging sequence optimization scheduling problem in WRSNs, considering the recovery of failed sensors after being charged. Therefore, we propose an adaptive quantum particle swarm optimization algorithm based on a fuzzy control strategy (AFQPSO). The AFQPSO incrementally adjusts the Quadratic Parameter of Charging Timeliness about Network (QPCTN) using fuzzy reasoning. Experimental results demonstrate that the AFQPSO-MCS achieves a smaller QPCTN and exhibits superior global search ability.

关键词： Wireless rechargeable sensor network Mobile charging quantum particle swarm optimization algorithm Fuzzy control Sequence scheduling

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Fracture performance prediction of polyvinyl alcohol fiber-reinforced cementitious composites containing nano-SiO₂ using least-squares support vector machine optimized with quantum-behaved particle swarm optimization algorithm

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THEORETICAL AND APPLIED FRACTURE MECHANICS 2021年 115卷 103074-103074页

作者： Liu, Tingyu Zhang, Peng Cui, Guo Yue, Xiaodong Zhengzhou Univ Sch Water Conservancy Engn Zhengzhou 450001 Peoples R China Beijing Municipal Rd & Bridge Grp Co Ltd Beijing 100032 Peoples R China

Based on least-squares support vector machine optimized using a quantum particle swarm optimization algorithm (QPSO-LSSVM), a prediction model was established to predict the fracture properties of polyvinyl alcohol fiber-reinforced cementitious composites (CCs) containing nano-SiO2 (PVA-CCNS) and improve its accuracy and effectiveness. Nineteen groups of measured data obtained from fracture performance tests on a three-point bending notched PVA-CCNS beam were selected for analysis and prediction. The prediction results of the QPSO-LSSVM model were compared with those of the least-squares support vector machine optimized with the particle swarm optimization algorithm, least-squares support vector machine and back-propagation neural network models. The simulation analysis results indicated that the goodness of fit (R-2) values of the fracture energy, initial fracture toughness and unstable fracture toughness were 0.790, 0.940 and 0.950, respectively, for the QPSO-LSSVM prediction model. In addition, the fitting degree between the measured and predicted values of the QPSO-LSSVM prediction model was better than those of the other three models. The higher accuracy, better convergence, and robustness of the QPSO-LSSVM model than the other three models proves that the QPSO-LSSVM model is an optimal method for predicting the fracture performance of CCs. The proposed model can guide the mix proportion design of CC mixtures.

关键词： quantum particle swarm optimization algorithm Nano-SiO2 Polyvinyl alcohol fiber Fracture performance Least-squares support vector machine model

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Virtual Cluster Partitioning Method of Active Distribution Networks Using quantum particle swarm optimization and Sector Search

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ENERGY SCIENCE & ENGINEERING 2025年第4期13卷 1883-1895页

作者： Liu, Wei Tong, Yifan Xing, Xinran Chen, Xi Hu, Bo Sun, Qian Wang, Yufei Li, Huaxin Guo, Huidong Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang Liaoning Peoples R China

The presence of numerous distributed power sources in distribution grids leads to a diverse array of controlled object points and significant uncertainties, thereby posing a series of challenges to the control and operation of distribution grids. Hence, this study proposes a virtual cluster partitioning model for active distribution networks using a quantum particle swarm optimization (QPSO) algorithm and sector search, aiming to achieve autonomy within clusters and coordination between clusters. First, the article proposes a sector search model that transforms the topological connections of the distribution network into mathematical expressions. This model simplifies the search for node locations and improves the algorithm's convergence speed. Building upon the traditional particle swarm optimization (PSO) algorithm, this study introduces the wave function and Schr & ouml;dinger equation to enhance algorithm performance. By treating the vectors obtained from sector searches as particles, the proposed QPSO algorithm significantly improves both the search efficiency and global convergence in solving the virtual cluster partitioning model. Finally, case studies conducted on the modified PG&E 69-node system demonstrated the proposed method's significant advantages. The method improved computational efficiency, with a cluster power supply rate over 0.6 and modularity above 0.7, ensuring balanced partitioning. The scalability and effectiveness of the proposed method were validated on an 85-node system, achieving balanced cluster partitioning with high operational efficiency and adaptability.

关键词： new distribution network quantum particle swarm optimization algorithm sector search model virtual cluster partition

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State-of-health estimation of lithium-ion batteries based on QPSO-BPNN

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IONICS 2025年第2期31卷 1437-1449页

作者： Yao, Yongming Li, Fei Li, Haofa Liu, Junchi Wang, Xindi Li, Tianyu Jilin Univ Sch Mech & Aerosp Engn Changchun 130022 Peoples R China

Lithium battery state of health (SOH) estimation is crucial to ensure the safe and reliable operation of the battery. To enhance the accuracy of lithium battery SOH estimation, a model for estimating the state of health of lithium-ion batteries based on quantum particle swarm optimization (QPSO) optimized backpropagation neural network (BPNN) was proposed. Initially, the degradation mechanism of lithium-ion batteries is analyzed. Subsequently, BPNN is utilized to learn from the database training samples, quantum particle swarm optimization is employed to determine the optimal connection threshold and weight, and the key parameters of the model are optimized by QPSO to improve the estimation accuracy of the model. Finally, the SOH estimation result of the lithium battery is obtained. The NASA public dataset was utilized to validate the model. The results indicate that the proposed model exhibits higher accuracy compared to prediction models utilizing standard particle swarm optimization, dung beetle algorithm, and seagull algorithm optimization BPNN on various lithium-ion datasets. The average absolute error, root mean square error, and average relative percentage error are maintained within 0.00805, 0.01140, and 1.30850%, respectively, demonstrating the effective enhancement of the estimation accuracy of lithium-ion battery SOH.

关键词： Lithium-ion batteries Health status estimates quantum particle swarm optimization algorithm BP neural network

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Cable Tension of Long-Span Steel Box Tied Arch Bridges Based on Radial Basis Function-Support Vector Machine Optimized by quantum-Behaved particle swarm optimization

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7163页

作者： Shi, Hongcai Shi, Menglin Xu, Weisheng Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China

To investigate hanger force during the construction phase of large-span steel box tie arch bridges, the challenge of low accuracy in force identification due to multifactor coupling was addressed. An energy method was employed to derive formulas for calculating forces under different boundary conditions. Utilizing the QPSO-RBF-SVM machine learning algorithm model, predictions of bridge formation stage forces were conducted, integrating findings from actual engineering case studies. Error analysis on hanger force was performed, revealing that the quantum particle swarm optimization (QPSO) algorithm optimizes parameters in the radial basis function support vector machine (RBF-SVM). The model was trained on datasets, achieving an average relative error of 0.65% in predicted cable force values compared with measured values in the test set, with a coefficient of determination of 0.97. These results demonstrate superior accuracy compared with calculations derived from the energy method and other machine learning algorithms. This algorithmic model presents a promising approach for accurately assessing cable forces in large-span steel box tie arch bridges.

关键词： tied arch bridges prediction of cable forces energy method quantum particle swarm optimization algorithm radial basis functions support vector machines

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An Improved quantum particle swarm algorithm Optimized Regularized Extreme Learning Machine for Short-Term Load Forecasting

An Improved Quantum Particle Swarm Algorithm Optimized Regul...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Chen Yonglong Zhu Jinda Yang Dongmei Wang Xinping NARI Grp Corp Lab Integrated Energy Syst Simulat & Operat Contr Nanjing Peoples R China

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152813

In order to screen out the nonlinear relationship in massive load data more quickly and improve the accuracy of short-term load forecasting model, the genetic quantum particle swarm optimization algorithm (GAQPSO) is proposed to optimize the input weight and hidden layer deviation of the regularized extreme learning machine (RELM) based on the shortcomings of quantum particle swarm optimization algorithm (QPSO) in dealing with complex high-dimensional parameter optimization problems, which forms a hybrid short-term load forecasting model called GAQPSO-RELM. Meanwhile, when the input features are selected, the influences of historical load, temperature, time interval and type of date are fully considered, so the accuracy of the short-term load forecasting model is further improved. The experimental result shows that the proposed short-term load forecasting model has a higher accuracy than the QPSO-RELM model and the common RELM model. Besides, it can better reflect the changeable trend of the daily load curve, which verifies the effectiveness of the proposed predictive model.

关键词： genetic algorithm quantum particle swarm optimization algorithm regularized extreme learning machine short-term load forecasting

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An improved quantum particle swarm photovoltaic multi-peak mPPT method combined with Levy flight

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ENERGY SCIENCE & ENGINEERING 2020年第11期8卷 3980-3994页

作者： Chen, Lei Li, Zhijun Zhang, Yinan Zhang, Yi Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin 300130 Peoples R China North China Univ Sci & Technol Grad Sch Tangshan Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan Peoples R China

Inertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi-peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Levy flight strategy with DCWQPSO algorithm. Levy flight is a non-Gaussian random process. The algorithm introduces Levy flight strategy to change the mutation formula of particles and uses the characteristics of Levy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast-tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness.

关键词： hyperparameter Levy flight MPPT partial shading photovoltaic quantum particle swarm optimization algorithm

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An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2018年第13期232卷 2352-2368页

作者： Huang, Dawen Yang, Jianhua Zhang, Jingling Liu, Houguang China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou Jiangsu Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

The general scale transformation (GST) method is used in the bistable system to deal with the weak high-frequency signal submerged into the strong noisy background. Then, an adaptive stochastic resonance (ASR) method with the GST is put forward and realized by the quantum particle swarm optimization (QPSO) algorithm. Through the bearing fault simulation signal, the ASR method with the GST is compared with the normalized scale transformation (NST) stochastic resonance (SR). The results show that the efficiency of the GST method is higher than the NST in recognizing bearing fault feature information. In order to simulate the actual engineering environment, both the adaptive GST and the NST methods are implemented to deal with the same experimental signal, respectively. The signal-to-noise ratio (SNR) of the output is obviously improved by the GST method. Specifically, the efficiency is improved greatly to extract the weak high-frequency bearing fault feature information. Moreover, under different noise intensities, although the SNR is decreased versus the increase of the noise intensity, the ASR method with the GST is still better than the traditional NST SR. The proposed GST method and the related results might have referenced value in the problem of weak high-frequency feature extraction in engineering fields.

关键词： General scale transformation adaptive stochastic resonance bearing fault feature extraction quantum particle swarm optimization algorithm

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Research on static fault-tolerant control method of thruster based on MPC

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2021年第3期26卷 861-871页

作者： Ding, Xuelian Zhu, Daqi Yan, Mingzhong Shanghai Maritime Univ Shanghai Engn Res Ctr Intelligent Maritime Search Haigang Ave 1550 Shanghai 201306 Peoples R China

To ensure that the unmanned underwater vehicles (UUVs) can still successfully complete the corresponding tasks in the case of thrusters having faults, a novel fault-tolerant control strategy which combines the model predictive control (MPC) algorithm and the fault-tolerant reconstruction algorithm is presented in this paper. Firstly, a cascade control method based on model predictive control algorithm is introduced, and then the problem when the thrusters in the faulty condition are discussed. For different degrees of thruster fault, the method of weighted pseudo inverse and quantum particle swarm optimization (QPSO) is used for hybrid fault-tolerant control (FTC). At the same time, to overcome the limitations brought by the pseudo inverse reconstruction algorithm, an optimization criterion with the infinite norm as the cost function is introduced into the QPSO algorithm to accelerate the search for the optimal solution in the feasibility space so as to ensure the feasibility of the solution. The simulation results show that the fault-tolerant control method proposed based on MPC (FTC-MPC) in this paper can provide ideal control effects for the unmanned underwater vehicles.

关键词： Model predictive control algorithm Fault-tolerant reconstruction Weighted pseudo inverse quantum particle swarm optimization algorithm Unmanned underwater vehicles

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Simultaneous optimal location and sizing of DGs in distribution system considering different types of MGs in an electricity market

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2443-2459页

作者： Dashtaki, Amir Ali Hakimi, Seyed Mehdi Farahani, Elnaz Shahrabi Hassanzadehfard, Hamid Great Tehran Elect Distribut Co Tehran Iran Islamic Azad Univ Dept Elect Engn Damavand Branch Damavand Iran Islamic Azad Univ Renewable Energy Res Ctr Damavand Branch Damavand Iran Islamic Azad Univ Dept Elect Engn Miyaneh Branch Miyaneh Iran

The implementation of distributed generation (DG) units in power systems has been increasing in the last years. Among many of their advantages, voltage improvement and loss reduction can be obtained by the optimum inserting DGs into distribution networks (DNs). The present study suggests a methodology for optimization of DGs in the DN. Various types of microgrids are also considered to be connected to the DN and can inject electricity into the system in a deregulated electricity market. The proposed methodology provides the possibility of comparing, purchasing electricity from the microgrids, installing any distributed generation units, injecting electrical power from the main bus, as well as the penalty for environmental emissions, power losses, fuel consumption, and etc. The simulation results depict that as the microgrids are considered in a grid-connected mode in the distribution system, the power losses reduce from 1100 to 850 kW. In addition, if the DN is responsible for providing the loads of the MGs, the amount of fuel consumption is equal to 42.167 M-Liters during a year. In addition, there is about 27% reduction in the fuel consumption when the optimum interaction between the microgrids and the system are considered.

关键词： Electricity market Distributed generation Distribution system quantum particle swarm optimization algorithm

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