scholarly journals Structural Reliability Assessment by Multimodal Analysis Using Cellular Automaton Particle Swarm Optimization

2015 ◽  
Vol 64 (3) ◽  
pp. 190-195
Author(s):  
Ken ISHIBASHI ◽  
Hitoshi FURUTA ◽  
Yasutoshi NOMURA ◽  
Koichiro NAKATSU ◽  
Kyosuke TAKAHASHI
Keyword(s):  
Particle Swarm Optimization ◽  
Cellular Automaton ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multimodal Analysis

An efficient particle swarm optimization-joint probability distribution optimization method for structural reliability analysis

2020 ◽  
pp. 095440622094155
Author(s):  
Hossein Mansourinejad ◽  
Kamran Daneshjou
Keyword(s):  
Particle Swarm Optimization ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Particle Swarm ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Engineering Structures ◽  
Swarm Optimization ◽  
Highly Nonlinear ◽  
Conventional Methods

The performance function of many engineering structures and mechanisms is usually complex, highly nonlinear, and described in the implicit form. The reliability analysis of these structures using common methods requires high cost and time. In this paper, a new approach for reliability analysis of engineering structures and mechanisms by using the particle swarm optimization algorithm is presented. The advantages of this method in comparison with the conventional methods are its simplicity and accuracy. In addition, the limitations of the common previously presented methods are eliminated by the proposed method. This approach is based on a new redefinition of most probable point in the reliability analysis. To evaluate the performance and validity of the proposed method, some examples in the reliability analysis of various functions are employed. Finally, the superiority of the proposed method in performance and accuracy is demonstrated and compared to the conventional methods and it can be used for reliability analysis of complicated engineering structures.

Sequential Particle Swarm Optimization and Reliability Assessment of Planar-Type Voice Coil Motor

2013 ◽  
Author(s):  
Haikun Wang ◽  
Hong-Zhong Huang ◽  
Huanwei Xu ◽  
Zhonglai Wang ◽  
Xiaoling Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Force Constant ◽  
Reliability Assessment ◽  
Particle Swarm ◽  
Voice Coil Motor ◽  
Sequential Optimization ◽  
Reliability Based Design Optimization ◽  
Swarm Optimization ◽  
Reliability Based Design

Planar-type voice coil motor (VCM) is a key component in ultra-precision motion of fine stage of lithography machine. The reliability-based design optimization (RBDO) method given in this work provides a novel criterion to ensure performance of Lorentz motors by evaluating the reliability of force constant. To solve the reliability based design optimization problem in discrete space with the speed of decoupled loop in sequential optimization and reliability assessment (SORA) for global solution, a Sequential Particle Swarm Optimization and Reliability Assessment method is proposed. The reliability boundary shift is put into penalty function for constrained optimization in fitness evaluation of particle swarm optimization (PSO). The presented optimization design model, the geometric parameters of the studied Planar-type VCM in finite element model are treated as design variables whereas the thrust force constant is an output quantity of interests. By using electromagnetic analysis, the desired requirements of Lorentz motors are verified.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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