scholarly journals An improved particle swarm optimization algorithm for dynamic analysis of chain drive based on multidisciplinary design optimization

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982961
Author(s):  
Mengjiang Chai ◽  
Yongliang Yuan ◽  
Wenjuan Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Method ◽  
Drive System ◽  
Multidisciplinary Design ◽  
Chain Drive ◽  
Swarm Optimization

Chain drive is one of the most commonly used mechanical devices in the main equipment transmission system. In the past decade, scholars focused on basic performance research, but ignore its best performance. In this study, due to the large vibration of the chain drive in the transmission system, the vibration performance and optimization parameters are also considered as a new method to design the chain drive system to obtain the best performance of the chain drive system. This article proposes a new method and takes a chain drive design as a case based on the multidisciplinary design optimization. The system optimization objective and sub-systems are established by the multidisciplinary design optimization method. To obtain the best performance for the chain, the chain drive is executed by an improved particle swarm optimization algorithm. Dynamic characteristics of the chain drive system are simulated based on the multidisciplinary design optimization results. The impact force of the chain links, vibration displacement, and the vibration frequency are analyzed. The results show that the kinematics principle of the chain drive and the optimal parameter value are obtained based on the multidisciplinary design optimization method.

Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

2012 ◽  
Vol 226 (9) ◽  
pp. 2281-2295 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
Jafar Roshanian ◽  
Meisam Babaie ◽  
Parviz M Zadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Fuzzy Decision ◽  
Swarm Optimization ◽  
Multi Objective

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

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.

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