Running-Time Analysis of Particle Swarm Optimization with a Single Particle Based on Average Gain

2017 ◽  
pp. 515-527
Author(s):  
Wu Hongyue ◽  
Huang Han ◽  
Yang Shuling ◽  
Zhang Yushan
Keyword(s):  
Particle Swarm Optimization ◽  
Single Particle ◽  
Particle Swarm ◽  
Time Analysis ◽  
Swarm Optimization ◽  
Running Time ◽  
Average Gain ◽  
Running Time Analysis

Particle Swarm Optimization Algorithm with Mutation Operator for Particle Filter Noise Reduction in Mechanical Fault Diagnosis

2020 ◽  
Vol 34 (10) ◽  
pp. 2058012 ◽  
Author(s):  
Hanxin Chen ◽  
Dong Liang Fan ◽  
Lu Fang ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Noise Reduction ◽  
Real Time ◽  
Particle Swarm ◽  
Utilization Rate ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Running Time ◽  
Filter Noise

In this paper, a new particle swarm optimization particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with mutation operator, and is used for real-time filtering and noise reduction of nonlinear vibration signals. Because of its introduction of mutation operator, this algorithm overcomes the problem where by particle swarm optimization (PSO) algorithm easily falls into local optimal value, with a low calculation accuracy. At the same time, the distribution and diversity of particles in the sampling process are improved through the mutation operation. The defect of particle filter (PF) algorithm where the particles are poor and the utilization rate is not high is also solved. The mutation control function makes the particle set optimization process happen in the early and late stages, and improves the convergence speed of the particle set, which greatly reduces the running time of the whole algorithm. Simulation experiments show that compared with PF and PSO-PF algorithms, the proposed NPSO-PF algorithm has lower root mean square error, shorter running time, higher signal-to-noise ratio and more stable filtering performance. It is proved that the algorithm is suitable for real-time filtering and noise reduction processing of nonlinear signals.

scholarly journals Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Hong Chen ◽  
Ying-ping Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Experiments ◽  
Particle Interaction ◽  
Particle Swarm ◽  
Convergence Time ◽  
Statistical Interpretation ◽  
Time Analysis ◽  
Swarm Optimization ◽  
The Social ◽  
Theoretical Results

We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.

Improved Particle Swarm Optimization Based on the Geometric Analysis of Single Particle

2014 ◽  
Vol 543-547 ◽  
pp. 1832-1839
Author(s):  
Yong Cai Ao ◽  
Yi Bing Shi ◽  
Wei Zhang ◽  
Yan Jun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Single Particle ◽  
Geometric Analysis ◽  
Particle Swarm ◽  
Nonlinear Problems ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization ◽  
Global Optimal ◽  
Particle Search

To enhance the ability of the standard Particle Swarm Optimization (PSO) on solving the complex nonlinear problems, an improved algorithm with independent inertia weight for each particle was proposed in this paper. Firstly, a single particle of the particle swarm was studied. The law of the instantaneous movement of single particle at each iteration was analyzed in a novel geometric view. Two importance conclusions were drawn: 1) The introduction of the two random numbers makes the particle search in a broad parallelogram area, while the product of the inertia weight and the particles present speed is the parallel shift of the aforementioned parallelogram area; 2) The search ability will be greatly reduced as soon as a particle becomes the Global Optimal Particle (GOP). Then, on the ground of the significant conclusions, the improvement plan of the standard PSO was put forward. Experiments show that the proposed algorithm has obvious superiority on the convergence ratio, the convergence speed and the convergence stability.

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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