Optimal network localization by particle swarm optimization

2011 ◽  
Author(s):  
Catalin V. Rusu ◽  
Hyo-Sung Ahn
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Localization ◽  
Optimal Network

Solving Large-Scale Heat Exchanger Network Synthesis Problems Using Particle Swarm Optimization

2011 ◽  
Vol 148-149 ◽  
pp. 636-640 ◽  
Author(s):  
Zhao Yi Huo ◽  
Liang Zhao ◽  
Hong Chao Yin
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Large Scale ◽  
Particle Swarm ◽  
Network Synthesis ◽  
Heat Exchanger Network ◽  
Swarm Optimization ◽  
Optimal Network ◽  
Simultaneous Synthesis ◽  
Heat Exchanger Network Synthesis

Heat exchanger network synthesis has been one of the most popular subjects in process design over the last 50 years. Various studies and optimization techniques have been proposed for designing optimal network with minimum total annual cost. Simultaneous synthesis approach via mathematical programming aims to find the optimal network without decomposition, which has been paid more attentions on the research recently. However, these methods might be not solvable or inefficient for large-scale problems. This paper makes an attempt to construct simultaneous synthesis model with split streams and to develop an efficient optimization framework based on particle swarm optimization for large-scale heat exchanger network synthesis problems. One example including 20 process streams is solved to give an illustration of the method.

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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