An Improved Particle Swarm Optimization Algorithm for Parameters Identification of Power Load Model Based on Simulated Annealing

2015 ◽  
Vol 12 (17) ◽  
pp. 6447-6454
Author(s):  
Renjie Song
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameters Identification ◽  
Swarm Optimization ◽  
Load Model ◽  
Improved Particle Swarm Optimization ◽  
Power Load

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.

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