Application of multi-phase particle swarm optimization technique to retrieve the particle size distribution

2008 ◽  
Vol 6 (5) ◽  
pp. 346-349 ◽  
Author(s):  
齐宏 齐宏 ◽  
阮立明 阮立明 ◽  
王圣刚 王圣刚 ◽  
史萌 史萌 ◽  
赵辉 赵辉 ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Swarm Optimization ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Phase Particle ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Multi Phase

scholarly journals Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm

2015 ◽  
Vol 19 (6) ◽  
pp. 2151-2160 ◽  
Author(s):  
Hong Qi ◽  
Zhen-Zong He ◽  
Shuai Gong ◽  
Li-Ming Ruan
Keyword(s):  
Particle Size ◽  
Particle Swarm Optimization ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Particle Swarm ◽  
Incident Laser ◽  
Swarm Optimization ◽  
N Distribution ◽  
Repulsive Particle Swarm Optimization ◽  
Inverse Results

The particle size distribution (PSD) plays an important role in environmental pollution detection and human health protection, such as fog, haze and soot. In this study, the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm and the basic PSO were applied to retrieve the PSD. The spectral extinction technique coupled with the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law were employed to investigate the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the Rosin-Rammer (R-R) distribution, the normal (N-N) distribution, the logarithmic normal (L-N) distribution were studied in the dependent model. Then, an optimal wavelengths selection algorithm was proposed. To study the accuracy and robustness of the inverse results, some characteristic parameters were employed. The research revealed that the ARPSO showed more accurate and faster convergence rate than the basic PSO, even with random measurement error. Moreover, the investigation also demonstrated that the inverse results of four incident laser wavelengths showed more accurate and robust than those of two wavelengths. The research also found that if increasing the interval of the selected incident laser wavelengths, inverse results would show more accurate, even in the presence of random error.

Materialized View Selection Using Swap Operator Based Particle Swarm Optimization

2021 ◽  
Vol 13 (1) ◽  
pp. 58-73
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Selection Problem ◽  
Data Repository ◽  
Swarm Optimization ◽  
View Selection ◽  
Particle Swarm Optimization Technique ◽  
Swap Operator

The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.

Sign in / Sign up

Export Citation Format

Share Document