Using an Improved Particle Swarm Optimization for Back Analysis of Geotechnical Parameters of Concrete Face Rock-fill Dams

2006 ◽  
Author(s):  
Hao Du ◽  
Shichun Chi ◽  
Feng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Back Analysis ◽  
Swarm Optimization ◽  
Geotechnical Parameters ◽  
Improved Particle Swarm Optimization ◽  
Rock Fill ◽  
Concrete Face

Back-Analysis Improved Particle Swarm Optimization Algorithm on Mechanical Parameters of Divisional Geotechnical Engineering Material

2012 ◽  
Vol 182-183 ◽  
pp. 1647-1653
Author(s):  
Wei Hua Fang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Geotechnical Engineering ◽  
Particle Swarm ◽  
Back Analysis ◽  
Mechanical Parameters ◽  
Engineering Material ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

In order to obtain geotechnical engineering material mechanical parameters correctly by using back analysis and overcome shortcoming of ordinary Particle Swarm Optimization, Improved Particle Swarm Optimization (IPSO) algorithm is developed on the aspects such as Stretching Particle, Metropolis Algorithm and adaptive weight updating .at the same time, the algorithm is compared with Catastrophe Particle Swarm Optimization Algorithm (CPSO) and Quantum Particle Swarm Optimization Algorithm(QPSO). Also result of back analysis was compared with that of Ultrasonic Testing and that of mixed-model of dam monitoring. The analysis shows that IPSO has better performance than that of PSO and CPSO, and considerable performance with QPSO.

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

Sign in / Sign up

Export Citation Format

Share Document