scholarly journals Network Life Time Analysis of WSNs Using Particle Swarm Optimization

2018 ◽  
Vol 132 ◽  
pp. 805-815 ◽  
Author(s):  
Amita Yadav ◽  
Suresh Kumar ◽  
Singh Vijendra
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Life Time ◽  
Time Analysis ◽  
Swarm Optimization ◽  
Network Life Time

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.

scholarly journals ER-KFPSO: Energetic Routing Technique Based Kernel Fuzzy Latency Particle Swarm Optimization Algorithm in Wireless Sensor Network

2020 ◽  
Vol 8 (6) ◽  
pp. 1196-1201
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Prediction Method ◽  
Life Time ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Fitness Value ◽  
The Stability

The applications of Wireless Sensor Networks in the recent times are wide in various places in sensor forms. Subsequently, the routing has to be done in a more energetic manner in line with the mobility and not compromising the QoS. Various techniques were proposed for the energetic routing in order to make a significant progress in the stability of the energy as it is a vital parameter of a WSN in times of data transmission for communication. These techniques are used for the life time enhancement of the sensor networks. Load balancing methods along with energetic routing are made used at the time of clustering. This article proposes an Energetic Routing which is based on the Kernel Fuzzy Latency PSO (ER-KFPSO) for supporting the energy consumption in Wireless Sensor Networks for improvisation in the life time of the network. The proposed method gives assistance in shaping the clusters by making use of the Energy Fitness value along with assignment of CHs. The proposed technique achieves better results on experiments when compared with the other existing methods that uses Particle Swarm optimization based nodes and life time prediction method through linkage (PNLP).

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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