An Improved Multi-swarm Particle Swarm Optimization Based on Knowledge Billboard and Periodic Search Mechanism

2017 ◽  
pp. 668-678
Author(s):  
Pan-pan Du ◽  
Fei Han
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Search Mechanism

Optimal Algorithm of Shuffled Frog Leaping Based on Immune Evolutionary Particle Swarm Optimization

2011 ◽  
Vol 268-270 ◽  
pp. 1188-1193 ◽  
Author(s):  
Zuo Yong Li ◽  
Chun Xue Yu ◽  
Zheng Jian Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Search Mechanism ◽  
Shuffled Frog Leaping ◽  
Searching Ability

In order to avoid premature convergence and improve the precision of solution using basic shuffled frog leaping algorithm (SFLA), based on immune evolutionary particle swarm optimization, a new shuffled frog leaping algorithm was proposed. The proposed algorithm integrated the global search mechanism in the particle swarm optimization (PSO) into SFLA, so as to search thoroughly near by the space gap of the worst solution, and also integrated the immune evolutionary algorithm into SFLA making immune evolutionary iterative computation to the optimal solution in the sub-swarm, so as to use the information of optimal solution fully. This algorithm can not only free from trapping into local optima, but also close to the global optimal solution with the higher precision. Calculation results show that the immune evolutionary particle swarm shuffled frog leaping algorithm (IEPSOSFLA) has the optimal searching ability and stability all the better than those of basic SFLA.

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

Particle Swarm Optimization with Hierarchical Structure

2009 ◽  
Vol 129 (3) ◽  
pp. 568-569
Author(s):  
Satoko Kinoshita ◽  
Atsushi Ishigame ◽  
Keiichiro Yasuda
Keyword(s):  
Particle Swarm Optimization ◽  
Hierarchical Structure ◽  
Particle Swarm ◽  
Swarm Optimization
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