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improved particle swarm optimization algorithm for AGV Path Planning

IEEE ACCESS 2021年 9卷 33522-33531页

作者： Tao Qiuyun Sang Hongyan Guo Hengwei Wang Ping Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China

In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a one-line production line in the workshop, establishes a mathematical model with the shortest transportation time as the objective function, and proposes an improved particle swarm optimization(IPSO) algorithm to obtain an optimal path. In order to be suitable for solving the path planning problem, we propose a new coding method based on this algorithm, design a crossover operation to update the particle position, and adopt a mutation mechanism to avoid the algorithm from falling into the local optimum. By calculating the shortest transportation time obtained, the improved algorithm is compared with other intelligent optimization algorithms. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of related improvement mechanisms.

关键词： Machine tools Conferences Production Transportation Task analysis Heuristic algorithms Path planning Automated guided vehicle improved particle swarm optimization algorithm scheduling optimization routing plan

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improved particle swarm optimization algorithm for Adaptive Frequency-Tracking Control in Wireless Power Transfer Systems

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JOURNAL OF POWER ELECTRONICS 2018年第5期18卷 1470-1478页

作者： Li, Yang Liu, Liu Zhang, Cheng Yang, Qingxin Li, Jianxiong Zhang, Xian Xue, Ming Tianjin Polytech Univ Tianjin Key Lab Adv Elect Engn & Energy Technol Tianjin Peoples R China

Recently, wireless power transfer (WPT) via coupled magnetic resonances has attracted a lot of attention owing to its long operation distance and high efficiency. However, the WPT systems is over-coupling and a frequency splitting phenomenon occurs when resonators are placed closely, which leads to a decrease in the transfer power. To solve this problem, an adaptive frequency tracking control (AFTC) was used based on a closed-loop control scheme. An improved particle swarm optimization (PSO) algorithm was proposed with the AFTC to track the maximum power point in real time. In addition, simulations were carried out. Finally, a WPT system with the AFTC was demonstrated to experimentally validate the improved PSO algorithm and its tracking performance in terms of optimal frequency.

关键词： Adaptive frequency tracking improved particle swarm optimization algorithm Over coupling Wireless power transfer

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Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm

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Chinese Physics B 2023年第4期32卷 352-363页

作者：王奕涵章海锋 Bell Honors School of Nanjing University of Posts and Telecommunication Nanjing 210023China College of Electronic and Optical Engineering&College of Flexible Electronics Nanjing University of Posts and TelecommunicationsNanjing 210023China

Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized *** effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these *** with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute *** further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and *** is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed *** impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are ***,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.

关键词： magnetized plasma photonic crystals improved particle swarm optimization algorithm nonreciprocal ultra-wide band absorption angular insensitivity

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An improved particle swarm optimization algorithm for reliability problems

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ISA TRANSACTIONS 2011年第1期50卷 71-81页

作者： Wu, Peifeng Gao, Liqun Zou, Dexuan Li, Steven Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ S Australia Div Business Adelaide SA 5001 Australia

An improved particle swarm optimization (IPSO) algorithm is proposed to solve reliability problems in this paper. The IPSO designs two position updating strategies: In the early iterations, each particle flies and searches according to its own best experience with a large probability;in the late iterations, each particle flies and searches according to the fling experience of the most successful particle with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can not only prevent the IPSO from trapping into the local optimum, but also enhances its space developing ability. Experimental results show that the proposed algorithm has stronger convergence and stability than the other four particle swarm optimization algorithms on solving reliability problems, and that the solutions obtained by the IPSO are better than the previously reported best-known solutions in the recent literature. (C) 2010 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： improved particle swarm optimization algorithm Reliability problems Position updating Mutation Convergence

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Furnace flame recognition based on improved particle swarm optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2020年第8期234卷 888-899页

作者： Wang, Wei Peng, Chen Mi, Hanyu Chen, Chuanliang Zeng, Deliang Shanghai Univ Sch Mech & Elect Engn & Automat 99 Shangda Rd Shanghai 200444 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

Industrial furnace kiln internal combustion flame directly reflects the combustion of fuel quality and stability and determines the security of the whole production process. The flame image contains many important information that cannot be observed by people's eyes, as a result, how to effectively separate the flame image from the surrounding background by means of science and technology has the great research significance and application value. In this article, the idea of neighborhood particles is introduced into the standard particle swarm optimization algorithm, and a furnace flame recognition method is proposed based on improved particle swarm optimization algorithm. The method first uses red, green and blue color space to design the extraction model of flame image, then uses the proposed improved particle swarm optimization algorithm and Otsu algorithm to solve the optimal segmentation threshold involved in the model. Experimental results show that the proposed improved particle swarm optimization algorithm can always find the optimal segmentation threshold of the flame image within no more than 100 iterations and reduce the computation time nearly 0.01 s. Compared with the previous research results, the recognition rate of the extraction model designed in this article has been greatly improved to over 93%, which is of great value for the safe and stable operation of industrial furnaces.

关键词： Furnace flame recognition improved particle swarm optimization algorithm colorimetric principle

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A Routing-Based Repair Method for Digital Microfluidic Biochips Based on an improved Dijkstra and improved particle swarm optimization algorithm

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MICROMACHINES 2020年第12期11卷 1052页

作者： Zheng, Wenbin Shi, Jinlong Wang, Anqi Fu, Ping Jiang, Hongyuan Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Peoples R China

Digital microfluidic biochips (DMFBs) are attractive instruments for obtaining modern molecular biology and chemical measurements. Due to the increasingly complex measurements carried out on a DMFB, such chips are more prone to failure. To compensate for the shortcomings of the module-based DMFB, this paper proposes a routing-based fault repair method. The routing-based synthesis methodology ensures a much higher chip utilization factor by removing the virtual modules on the chip, as well as removing the extra electrodes needed as guard cells. In this paper, the routing problem is identified as a dynamic path-planning problem and mixed path design problem under certain constraints, and an improved Dijkstra and improved particle swarm optimization (ID-IPSO) algorithm is proposed. By introducing a cost function into the Dijkstra algorithm, the path-planning problem under dynamic obstacles is solved, and the problem of mixed path design is solved by redefining the position and velocity vectors of the particle swarm optimization. The ID-IPSO routing-based fault repair method is applied to a multibody fluid detection experiment. The proposed design method has a stronger optimization ability than the greedy algorithm. The algorithm is applied to 8x9, 8x8, and 7x8 fault-free chips. The proposed ID-IPSO routing-based chip design method saves 13.9%, 14.3%, and 14.5% of the experiment completion time compared with the greedy algorithm. Compared with a modular fault repair method based on the genetic algorithm, the ID-IPSO routing-based fault repair method has greater advantages and can save 39.3% of the completion time on average in the completion of complex experiments. When the ratio of faulty electrodes is less than 12% and 23%, the modular and ID-IPSO routing-based fault repair methods, respectively, can guarantee a 100% failure repair rate. The utilization rate of the electrodes is 18% higher than that of the modular method, and the average electrode usage time is 17%. T

关键词： DMFB fault repair routing-based improved Dijkstra algorithm improved particle swarm optimization algorithm

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Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第12期27卷 12842-12850页

作者： Fang, Ruiming Popole, Zouthi Huaqiao Univ Dept Elect Engn Xiamen 362021 Peoples R China

In order to make hydropower station's development and operation harmonious with ecological protection, the optimal operation of hydropower stations to meet the needs of ecological protection is studied. Firstly, the ecological protection function of river course is defined according to the minimum ecological runoff and suitable ecological runoff. Then, a multi-objective optimal running model of reservoir which can maximize the capacity of ecological protection and generation is proposed. Finally, an improved multi-objective particle swarm optimization algorithm (MOPSO), which can construct a neighborhood for each particle and choose the neighborhood optimal solution by adopting self-organizing mapping (SOM) method, is proposed to solve the model. The model is applied to the Shui-Kou Hydropower Station in Minjiang, China. The results show that the model can get the optimal schedule with balanced consideration of ecological benefits and power generation benefits, which has not a great impact on the economic benefits of reservoirs while achieving the goal of ecological environment. The research results can provide theoretical basis and concrete scheme reference for reservoir operation.

关键词： Hydropower reservoir Multi-objective optimized scheduling improved particle swarm optimization algorithm Ecological protection

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Research on structure optimization and motion characteristics of wearable medical robotics based on improved particle swarm optimization algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 129卷 187-198页

作者： Zheng, Yi Wang, Youqiang Liu, Jixin Qingdao Univ Technol Sch Mech & Automot Engn Qingdao 266520 Peoples R China Qingdao Huanghai Univ Inst Intelligence & Manufacture Qingdao 266427 Peoples R China

In order to solve the problem of movement coordination and stability between the wearable medical robotics and the wearer, analysis the structure of lower limbs and movement gait of human body, obtains the structure principle and motion characteristics of human lower limbs;Based on anthropomorphic design criteria, proposed a wearable medical robotics, use particle swarm optimization (PSO) to optimize the installation position of the hydraulic cylinder at hip joint and knee joint, The optimal parameters of the hip and knee variables were obtained through optimization. Adopt the idea of passive drive instead of active drive, designed the hydraulic drive system and control signal amplifying circuit, established the mathematical model of the four-way sliding valve with zero opening and analyzed its stability;Use AMESim software to build the model of the hydraulic system, and conducted the motion simulation test of the hydraulic system, the results show that:The working process of the hydraulic cylinder meets the characteristics of lower limbs movement of the wearer;built the experiment platform and conducted the walking attitude experiment, the result shows that:the pressure measured by the pressure sensor modules at both ends of the soles of the feet, and the mean pressure of the soles of the feet will fluctuate between 0 and 800 N with the motion of the man-machine system, the X-axis acceleration, Y-axis acceleration, Z-axis acceleration and the acceleration scalar are all changing within the range of -10 m/s2 similar to 20 m/s2, meeting the requirements of normal human movement. (C) 2021 Elsevier B.V. All rights reserved.

关键词： improved particle swarm optimization algorithm Wearable medical robotics Motion characteristics Structural optimization

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An improved particle swarm optimization algorithm for reliability-redundancy allocation problem with mixed redundancy strategy and tor heterogeneous components

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 181卷 62-74页

作者： Ouyang, Zhiyuan Liu, Yu Ruan, Sheng-Jia Jiang, Tao Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China

The reliability-redundancy allocation problem (RRAP) has been intensively studied to identify the optimal system design under several constraints, so as to maximize the system reliability as much as possible. Recently, the mixed redundancy strategy where both active redundant components and cold standby components can be simultaneously used by a subsystem has been introduced into the RRAP, leading to a more superior result than that of a subsystem with either active redundancy strategy or cold standby strategy. Nevertheless, in the existing studies, all the components deployed in a subsystem have to be homogeneous even if the mixed redundancy strategy is chosen. The strong premise is released in this paper by introducing heterogeneous components into the RRAP. The resulting optimization problem is resolved by proposing an improved particle swarm optimization (PSO) algorithm with stochastic perturbation nature. The efficiency of the proposed algorithm has been demonstrated by conducting a set of comparative studies on three benchmark problems. As observed in our study, the design flexibility introduced by our method can always yield a greater system reliability than that of the state-of-the-art methods. Most importantly, we also reveal that the switching reliability has significant influence on the optimal system configuration.

关键词： Reliability-redundancy allocation problem Mixed redundancy strategy Heterogeneous components improved particle swarm optimization algorithm

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Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第16期43卷 3647-3662页

作者： Tian, Zhongda Shenyang Univ Technol Sch Artificial Intelligence Room 511111Shenliao West Rd Shenyang 110870 Peoples R China

In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.

关键词： Short-term wind power prediction kernel principal component analysis echo state network improved particle swarm optimization algorithm

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