Hardware Architecture for Particle Swarm Optimization Using Floating-Point Arithmetic

2009 ◽  
Author(s):  
Daniel M. Muñoz Arboleda ◽  
Carlos H. Llanos ◽  
Leandro dos_S. Coelho ◽  
Mauricio Ayala-Rincón
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Hardware Architecture ◽  
Floating Point ◽  
Swarm Optimization ◽  
Floating Point Arithmetic ◽  
Point Arithmetic

scholarly journals Comment on “An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps”

2015 ◽  
Vol 2015 ◽  
pp. 1-3
Author(s):  
Yudong Zhang ◽  
Genlin Ji ◽  
Zhengchao Dong ◽  
Shuihua Wang ◽  
Preetha Phillips
Keyword(s):  
Particle Swarm Optimization ◽  
Chaotic Maps ◽  
Particle Swarm ◽  
Gauss Map ◽  
Floating Point ◽  
Swarm Optimization ◽  
The Difference ◽  
Definition Of

This paper researched three definitions of Gauss map and found that the definition of “Gauss map” in the paper of Arasomwan and Adewumi may be incoherent with other publications. In addition, we analyzed the difference of continuous Gauss map and the floating-point Gauss map, and we pointed out that the floating-point simulation behaved significantly differently from the continuous Gauss map.

scholarly journals A Flexible Hardware Architecture for Particle Swarm Optimization

2012 ◽  
Vol 16 (6) ◽  
pp. 519-526 ◽  
Author(s):  
Kui-Ting Chen ◽  
Molin Jia ◽  
Jiaqi Zhang ◽  
Takaaki Baba
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Hardware Architecture ◽  
Swarm Optimization ◽  
Flexible Hardware

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

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