Structural Optimization of Laminated Composite Blade Using Particle Swarm Optimization

2012 ◽  
Author(s):  
H. Li ◽  
K. Chandrashekhara
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Laminated Composite ◽  
Failure Criteria ◽  
Blade Design ◽  
Loading Conditions ◽  
Swarm Optimization ◽  
Composite Failure ◽  
Composite Blade ◽  
Discrete Pso

Composite blades working underwater experience complicated loading conditions. Robust design of a composite blade for hydrokinetic applications should satisfy varying loading conditions and conservative failure evaluations. Blade manufacturing using composites requires extensive optimization studies in terms of composite materials, number of layers, stacking sequences, ply thickness and orientation. In the current study, particle swarm optimization (PSO) technique is adopted to conduct composite lay-up optimization for the turbine blade. Layer numbers, ply thickness and ply orientations are optimized using standard PSO (SPSO) to minimize weight. Composite failure criteria are applied using finite element method to generate the most conservative blade design. Based on the blade lay-up design with minimized weight, stacking sequence of the blade lay-up was optimized to maximum safety factor of the designed blade using permutation discrete PSO (PDPSO). To improve the efficiency of the algorithm, the concepts of valid/invalid exchange, and memory checking were introduced into PDPSO. Meanwhile, another discrete PSO using partially mapped crossover (PMX) technique was used to validate the simulation results optimized by PDPSO. A final composite blade design with minimized weight and maximized load-carrying capacity was presented.

A Discrete Particle Swarm Optimization Approach to Optimize the Assembly Sequence of Mechanical Product

2012 ◽  
Vol 490-495 ◽  
pp. 203-207
Author(s):  
Zhong Bo Zhang ◽  
Chuan Yong Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Assembly Sequence Planning ◽  
Optimization Approach ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Discrete Pso ◽  
The Difference ◽  
Assembly Tool

The aim of assembly sequence planning (ASP) is to achieve the best assembly sequence which assembly cost and time used is less. The geometrical feasibility of an assembly sequence is validated by the interference matrix of the product. The number of assembly tool changes and the number of assembly operation type changes are considered in the fitness function. To establish the mapping relation between ASP and particle swarm optimization (PSO) approach, some definitions of position, velocity and operator of particles are proposed. The difference of the proposed discrete PSO (DPSO) algorithm with the other algorithm is the emphasis on the geometrical feasibility of the assembly sequence. The geometrical feasibility is verified at the first and the every iteration. The performance and feasibility of the proposed algorithm is verified via a simplified engine assembly case.

CDMA Multiuser Detection Based on Improved Particle Swarm Optimization Algorithm

2011 ◽  
Vol 50-51 ◽  
pp. 3-7 ◽  
Author(s):  
Nan Ping Liu ◽  
Fei Zheng ◽  
Ke Wen Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Multiuser Detection ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Discrete Pso ◽  
Case Simulation ◽  
Multi Access

CDMA multiuser detection (MUD) is a crucial technique to mobile communication. We adopt improved particle swarm optimization (PSO) algorithm in MUD which incorporates factor and utilizes function to discrete PSO. Comparison of BER and near-far effect has verified its effectiveness on multi-access interference (MAI). The algorithm accelerates the convergent speed meanwhile it also displays feasibility and superiority in case simulation.

Study on Discrete Particle Swarm Optimization Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 1787-1794
Author(s):  
Bei Zhan Wang ◽  
Xiang Deng ◽  
Wei Chuan Ye ◽  
Hai Fang Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Future Research ◽  
Continuous Variables ◽  
Swarm Optimization ◽  
Operation Rules ◽  
New Type ◽  
Discrete Pso ◽  
Searching Method

The particle swarm optimization (PSO) algorithm is a new type global searching method, which mostly focus on the continuous variables and little on discrete variables. The discrete forms and discretized methods have received more attention in recent years. This paper introduces the basic principles and mechanisms of PSO algorithm firstly, then points out the process of PSO algorithm and depict the operation rules of discrete PSO algorithm. Various improvements and applications of discrete PSO algorithms are reviewed. The mechanisms and characteristics of two different discretized strategies are presented. Some development trends and future research directions about discrete PSO are proposed.

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
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