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Equipment maintenance task prediction model analysis based on BAS-PSO hybrid optimization algorithm

SYSTEMS SCIENCE & CONTROL ENGINEERING 2021年第1期9卷 455-466页

作者： Song, Weixing Wu, Jingjing Chang, Huiqiang Xu, Guihua Army Engn Univ PLA Shijiazhuang Hebei Peoples R China Western Theater Army Dept Logist Lanzhou Peoples R China Unite 31682 PLA Lanzhou Peoples R China Unite 32272 PLA Lanzhou Peoples R China

Aiming at the maintenance task prediction problem of armored forces, the macro model and micro model are established to analyze the constraint conditions, and the equipment maintenance task prediction model is established in order to meet the motor hours echelon storage. Under the condition of meeting the balance of annual motor hours payments, the motor hours consumed by equipment are allocated according to the annual training tasks, and a hybrid optimization algorithm of improved particle swarm optimization is designed to solve the model, and a case study is carried out on a few vehicles in a certain army. The simulation results show that the model can effectively solve the problem of equipment maintenance task prediction, and a provide reference value for troops to make the maintenance plans.

关键词： Maintenance tasks motor hours balance of payments echelon storage equipment utilization beetle antennae search algorithm particle swarm optimization algorithm

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algorithms for solving assembly sequence planning problems

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NEURAL COMPUTING & APPLICATIONS 2021年第2期33卷 525-534页

作者： Su, Yingying Mao, Haixu Tang, Xianzhao Shenyang Univ Sch Mech Engn Shenyang 110044 Liaoning Peoples R China

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy-particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly

关键词： Assembly sequence planning particle swarm optimization algorithm Various population strategy Hybrid algorithm

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Discrete particle swarm optimization algorithm for Lot-streaming No-wait Flow Shop Scheduling Problem

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2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Yang, Lin Pan, Yuxia Hainan Univ Sanya Coll Sanya 572022 Hainan Peoples R China

ISBN: (纸本)9783037854471

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals. After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

关键词： particle swarm optimization algorithm lot-streaming no-wait flow shop scheduling problem local search

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Localization Technology Based on Quantum-behaved particle swarm optimization algorithm for Wireless Sensor Network

Localization Technology Based on Quantum-behaved Particle Sw...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Zhao, Ji Fu, Yi Wang, Han-bo Wuxi City Coll Vocat Technol Dept Elect Informat Engn Wuxi 214000 Jiangsu Peoples R China

ISBN: (纸本)9783037855034

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

关键词： quantum-behaved particle swarm optimization algorithm particle swarm optimization algorithm location wireless sensor networks

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A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods

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EARTH SCIENCE INFORMATICS 2021年第1期14卷 365-376页

作者： Kazemi, Mahsa Sheikh Banihabib, Mohammad Ebrarim Soltani, Jaber Univ Tehran Coll Aburaihan Dept Water Engn Tehran Iran

Since sediment concentration is an effective factor on increasing debris flood's peak flow and damages from floods, developing new models to predict the sediment concentration of debris floods has crucial importance. In this study, a hybrid SVR-PSO model was proposed to predict the concentration of sediment in typical and debris floods, and it was examined in three basins located in Gilan, Mazandaran, and Tehran Provinces, Iran. Mean elevation and slope of the basin, the area of the basin, current day's rainfall, the rainfall of previous days (1-3 days before flood) for all rain-gauge stations of the basins, as well as the discharge of the previous day, were used as the input variables of the model. Then, various combinations of variables were tested to assess the factors influencing the concentration of sediment in typical and debris floods in order to find the best variable combination with a high performance in predicting the concentration of sediment in the studied floods. The results showed that basin elevation, current day's rainfall, previous day's discharge, rainfall of the previous day, basin area, rainfall of the previous two days, basin slope, and rainfall of the previous three days were the key factors influencing the concentration of sediment in typical and debris floods, respectively. Coefficient of determination, root mean square error, and mean absolute percentage error were estimated 0.96, 0.003, and 14.38% for the proposed model at the testing phase, respectively. This implies model's good performance for predicting the concentration of sediment in typical and debris floods so that the present model can provide reliable predictions of flood character in basins.

关键词： Concentration of sediment Debris flood particle swarm optimization algorithm Prediction Support vector regression

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船用固体氧化物燃料电池与二冲程低速双燃料发动机集成动力系统的设计与优化

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哈尔滨工程大学学报（英文版） 2023年第4期22卷 837-849页

作者：曲金博冯永明吴云金朱元清邬斌扬肖忠旭 College of Power and Energy Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this ***,a 0-D model of SOFC and a 1-D model of ICE are built as agent ***,parameter analysis of the system is conducted based on SOFC and ICE *** show that the number of cells,current density,and fuel utilization can influence SOFC and ***,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this *** results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power ***,promising improvements in efficiency of 5.1%are achieved compared to the original ***,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.

关键词： Combined system SOFC-ICE integrated cycle Data-driven model particle swarm optimization algorithm

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Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

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AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Feng, Hao Jiang, Jinye Ding, Nan Shen, Fangping Yin, Chenbo Cao, Donghui Li, Chunbiao Liu, Tao Xie, Jiaxue Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Wuxi Cosmo Suspended Platform Co Ltd Wuxi 214128 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Changwang Sch Honors Nanjing 210044 Peoples R China Nanjing Tech Univ United Inst Excavator Key Technol Nanjing 211816 Peoples R China SANY Grp Co Ltd Suzhou 215300 Peoples R China

Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.

关键词： Excavator Hydraulic system Trajectory planning particle swarm optimization algorithm

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Traveling wave inversion method of power line faults

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期149卷

作者： Xia, Yixiang Li, Zewen Wang, Shuai Sun, Jian Guo, Xinyu Changsha Univ Sci & Technol Elect & Informat Engn Dept Changsha Peoples R China

In substations, the fault signal at the measuring point includes a variety of signal aliasing. This is partly due to the short length of the substation lines and the exitance of multiple types of equipment that cause the reflection and refraction of the traveling waves. Compared with the incident wave, the distortion in the fault signal is often significant hence interfering with the fault identification and location. To address this issue, we propose an inversion method of the incident wave. To do this, we first investigate the impact of multiple outlets of the substation bus, equivalent stray capacitance of the bus to the ground, transformers, line trap, and other types of equipment on traveling wave transmission. Modeling the aliasing effect of the traveling wave measuring point signal we then propose a fault signal filtering algorithm based on the improved Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT). Using this algorithm we then formulate an incident wave inversion model based on substation component parameters. The component parameters in the equivalent circuit model of substation are then accurately identified to achieve an accurate inversion of the incident waves. Identification of parameters is based on an optimization model of substation component parameters based on the particle swarm optimization algorithm (PSO). Our results show that compared with the measured mixed wave signal, the fault characteristics of the inverted incident waves are more accurate, hence improving the fault location accuracy.

关键词： Waveform inversion Incident wave Substation particle swarm optimization algorithm

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A tristable nonlinear energy sink

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2023年 238卷

作者： Zeng, You-cheng Ding, Hu Shanghai Univ Shanghai Inst Appl Math & Mech Sch Mech & Engn Sci Shanghai Key Lab Mech Energy Engn Yangchang Rd 149189 Shanghai 200444 Peoples R China

The tristable characteristics are mostly used in the field of energy harvesting and vibration isolator, but are rarely devoted to in the field of vibration absorption. In this paper, a tristable nonlinear energy sink (TNES) model is proposed for vibration absorption. The TNES is constructed by fixing a magnet in the middle of the clamping pre -pressure beam, and setting a magnet on both sides of the fixed magnet. By adjusting the magnet position, the proposed TNES can be transformed into bistable NES (BNES) and monostable NES (MNES). The TNES is coupled with the linear oscillator (LO), and the differential equation of motion of the coupled system is obtained with the Hamiltonian principle. The approximate analytical method and the numerical method are mutually verified. The vibration reduction efficiency of the TNES under harmonic excitation is presented. The influence of the pa-rameters of the TNES on the dynamics and vibration reduction efficiency is studied. The optimal parameters of the TNES are obtained by the particle swarm optimization (PSO) algorithm. Compared with the corresponding bistable NES and monostable NES, it shows that the tristable NES has obvious advantages in vibration sup-pression efficiency. The effects of the TNES barrier depths and the excitation amplitude on the vibration reduction efficiency are revealed. The TNES can dissipate energy through chaotic inter-well oscillation between three stable positions, and achieve the purpose of effectively suppressing vibration.

关键词： Nonlinear energy sink Tristable Vibration control Harmonic balance method particle swarm optimization algorithm

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Design and optimization of Personalized Education System Based on Intelligent algorithms

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Procedia Computer Science 2024年 243卷 514-522页

作者： Jie Zhu Yahong Liao Danni Lu Wuhan Railway Vocational College of Technology Wuhan 430205 Hubei China Xiamen Jin Shang Middle School Xiamen 361000 Fujian China Hubei Vocational College of Railway Transportation Wuhan 430070 Hubei China

The education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of rigidity, stiff, or resource scarcity. Therefore, this article aimed to establish a personalized education system to solve this problem and optimize the system based on intelligent algorithms. At the end of this article, a comparison was made on the algorithm performance of the decision tree algorithm in the intelligent algorithm. Compared with the original algorithm of the system, the accuracy increased from 70.35% to 75.68%. The system based on the intelligent algorithm also helped the students in the experimental class improve their grades, and even cleared the score record below 40 points, helping to improve the overall performance of the entire class.

关键词： Intelligent algorithm Decision Tree algorithm Personalized Education System particle swarm optimization algorithm

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