Maximizing Buckling Load Factors of Fiber-Placed Composite Cylindrical Shells by Particle Swarm Optimization

2015 ◽  
Author(s):  
Sedat Guldu ◽  
Altan Kayran
Keyword(s):  
Particle Swarm Optimization ◽  
Cylindrical Shells ◽  
Particle Swarm ◽  
Buckling Load ◽  
Swarm Optimization ◽  
Load Factors ◽  
Composite Cylindrical Shells

Best pattern for locating piezoelectric patches on a plate for maximum critical buckling loads, using particle swarm optimization algorithm

2018 ◽  
Vol 29 (14) ◽  
pp. 2874-2884 ◽  
Author(s):  
Javad Jafari Fesharaki ◽  
Hamed Mobini Dehkordi ◽  
Mohadeseh Zohari ◽  
Said Karimi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Free Edge ◽  
Buckling Load ◽  
Swarm Optimization ◽  
Critical Buckling Load ◽  
Linear Effect ◽  
Clamp Condition

Locating piezoelectric patches on structures under compressive forces as a sensor or actuator can predict or increase the buckling load of the structure. In this article, using particle swarm optimization algorithm, the location and pattern of piezoelectric patches on a plate to achieve maximum buckling load are investigated. For boundary condition of the plate, four different conditions are considered. The piezoelectric patches have more effect on critical buckling load for Clamp-Free condition and less effect for Clamp-Clamp condition. The voltage of patches has a linear effect on increasing the maximum critical buckling load. The results show that locating piezoelectric patches near the clamp edge has more influence on critical load than locating the patches near the simply support or free edge. The results in this article are validated by comparing to the results reported in the previous publication.

Buckling Load Maximization of Composite Laminates Using Particle Swarm Optimization With Various Constraints

2016 ◽  
Author(s):  
Tae-Uk Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Composite Laminates ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Analytic Solutions ◽  
Buckling Load ◽  
Element Analysis ◽  
Swarm Optimization ◽  
Augmented Lagrange Multiplier ◽  
Buckling Load Maximization

The stacking sequence of composite laminates is designed to have maximum buckling load using the particle swarm optimization (PSO) algorithm. The original PSO algorithm is modified to handle the discrete ply angles and the constraints such as stiffness and 4-ply contiguity requirements. For this, the augmented Lagrange multiplier (ALM) method is incorporated into the PSO algorithm. For the verification of the algorithm, the benchmarking problems are solved and the results are compared with the ones from the genetic algorithm or the analytic solutions. And then the laminates under in-plane compressive loadings are optimized for maximum buckling load considering the various constraints. The numerical results show that the algorithm finds the optimum with relatively small number of iterations with satisfying the constraints explicitly. Considering its advantage of derivative-free and simple procedures, the proposed algorithm can be applied to more complex models coupled with finite element analysis and various constraints.

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

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
Sign in / Sign up

Export Citation Format

Share Document