Modeling Data Center Temperature Profile in Terms of a First Order Polynomial RBF Network Trained by Particle Swarm Optimization

2018 ◽  
pp. 587-595
Author(s):  
Ioannis A. Troumbis ◽  
George E. Tsekouras ◽  
Christos Kalloniatis ◽  
Panagiotis Papachiou ◽  
Dias Haralambopoulos
Keyword(s):  
Particle Swarm Optimization ◽  
Temperature Profile ◽  
Data Center ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Rbf Network ◽  
First Order ◽  
Order Polynomial ◽  
Modeling Data

Integrative optimization by RBF network and particle swarm optimization

2009 ◽  
Vol 92 (12) ◽  
pp. 31-42 ◽  
Author(s):  
Satoshi Kitayama ◽  
Keiichiro Yasuda ◽  
Koetsu Yamazaki
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Rbf Network

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

scholarly journals Research on Adaptive Suppression of LCL Converter Resonance Grid-Connected System

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junwei Li ◽  
Yafang Tang ◽  
Junke Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stability Margin ◽  
System Stability ◽  
Current Feedback ◽  
Swarm Optimization ◽  
Lcl Filter ◽  
Rbf Network ◽  
Grid Impedance ◽  
Suppression Method

LCL-type converters are widely used in grid-connected systems due to their small size and good filtering performance. However, the resonance suppression problem brought by the LCL filter cannot be ignored. The capacitive current feedback is a commonly used resonance suppression method. In applications, the grid impedance can cause LCL filter resonance. Thus, this paper presents an adaptive resonance suppression method based on the RBF network optimized by particle swarm optimization. This method optimizes the initial parameters of the RBF network through particle swarm optimization, identifies the parameters of the PI controller by RBF neural network’s own identification capability, and updates the active damping coefficient based on constraints such as stability margin, thereby realizing the LCL-type inverter to maintain the system stability when the grid impedance changes. The effectiveness of the method is verified by experiments.

Optimizing the Data Center Energy Consumption Using a Particle Swarm Optimization-Based Approach

2016 ◽  
pp. 176-189 ◽  
Author(s):  
Cristina Bianca Pop ◽  
Viorica Rozina Chifu ◽  
Ioan Salomie Adrian Cozac ◽  
Marcel Antal ◽  
Claudia Pop
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Data Center ◽  
Particle Swarm ◽  
Swarm Optimization
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