Regrouping Optimization Method for Retired Batteries based on Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Xiangdong Li ◽  
Xu Chen ◽  
Yi Wang ◽  
Xiaoxia Si
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization

Research on Electronic Materials with Design of a Down-Conversion Mixer Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 771 ◽  
pp. 173-177
Author(s):  
Hui Lin Shan ◽  
Yin Sheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Frequency Conversion ◽  
Particle Swarm Optimization Algorithm ◽  
Electronic Materials ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Dielectric Substrate ◽  
Swarm Optimization

This paper presents principles of a down-converted mixer for four sub-harmonic and proposes a particle swarm optimization algorithm as a global search algorithm, and the performance equation is used as the assessment of the mixer circuit optimization method. Dielectric substrate adopts Electronic Materials with RF/Duroid 5880 whose dielectric constant is 2.20 and 5mil in thickness. The optimization algorithm can quickly get optimal results. The simulation results show that this mixer achieves higher 1 dB compression point, loss of frequency conversion which is less than 15 dB and good linearity.

Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm

2019 ◽  
Vol 30 (8) ◽  
pp. 1263-1275 ◽  
Author(s):  
Quan Zhang ◽  
Yichong Dong ◽  
Yan Peng ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Piezoelectric Ceramic ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Independent Particle ◽  
Hysteresis Characteristics

The hysteresis characteristics, which commonly existed in smart materials–based actuators, play a significant role in precision control technology. In this article, a modified Bouc–Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc–Wen hysteresis model are identified through a genetic algorithm–based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorithm–based particle swarm optimization features the strong searching ability both in early global search period and the later local search period. The experimental results show that the asymmetric Bouc–Wen model identified via genetic algorithm–based particle swarm optimization algorithm are more accurate than that identified through independent particle swarm optimization or genetic algorithm approach, and the maximum displacement error and the maximum relative error between the genetic algorithm–based particle swarm optimization model and the experimental value are 0.20 µm and 14.28%, respectively, which are much smaller than that of particle swarm optimization method with 0.67 µm and 47.85% and genetic algorithm method with 0.35 µm and 25%. In order to further verify the accuracy of the identified model, the hysteresis compensation of piezoelectric ceramic actuator was realized using the feedforward controller based on the inverse Bouc–Wen model.

Simulation for Automotive Engine Fault Diagnosis Method

2014 ◽  
Vol 687-691 ◽  
pp. 882-885
Author(s):  
Huan Xue Liu ◽  
Guang Dong Zhang ◽  
Zhen Zhong Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Accurate Data ◽  
Swarm Optimization ◽  
Automotive Engine ◽  
Diagnosis Method

For engine fault diagnosis problem, an engine fault diagnosis method based on particle swarm optimization algorithm is proposed. The velocity and spatial position of all the particles in the particle swarm are updated, in order to provide accurate data basis for the engine fault diagnosis. Particle swarm optimization method is utilized to process iteration for all particles, so as to determine whether failure exists in components of engine. Experimental results show that with the proposed algorithm to diagnose engine fault can effectively improve the accuracy of fault diagnosis, and achieved the desired results.

Source Mask Projector Optimization Method of Lithography Tools Based on Particle Swarm Optimization Algorithm

2017 ◽  
Vol 37 (10) ◽  
pp. 1022001 ◽  
Author(s):  
王 磊 Wang Lei ◽  
李思坤 Li Sikun ◽  
王向朝 Wang Xiangzhao ◽  
杨朝兴 Yang Chaoxing
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization

scholarly journals A Novel Particle Swarm Optimization Algorithm for Global Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Chun-Feng Wang ◽  
Kui Liu
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Standard Test ◽  
Test Functions ◽  
Swarm Optimization ◽  
Chaotic Search

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

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