Constrained improved particle swarm optimization algorithm for optimal operation of large scale reservoir: proposing three approaches

2017 ◽  
Vol 8 (4) ◽  
pp. 287-301 ◽  
Author(s):  
R. Moeini ◽  
M. Babaei
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Operation ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

scholarly journals Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Houxian Zhang ◽  
Zhaolan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Large Scale Network ◽  
Scale Network

No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

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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